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I don't have much comment on the reading, so I'll instead mention that 
Problem B4 on the Putnam today was, paraphrased:
   Let f(m,n) be the number of n-tuples of integers (x_1, x_2, ..., x_n) 
which satisfy |x_1| + |x_2| + ... + |x_n| <= m. Show that f(m,n) = f(n,m).

... Sounds like a good final exam question to me ;)

Jason.

From kevin08@MIT.EDU Sun Dec  4 05:29:38 2005
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Subject: [Hanson] Week's reading comments
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Hi,

I had a general question about this week's readings--it seems that 
Chebyshev's Inequality (S7, Pg. 12) is always tighter than Markov's 
Inequality (S6, pg. 10). Is there a simple proof of this, or is it even true?

Thanks,
Kevin


From ajshafer@MIT.EDU Sun Dec  4 11:50:07 2005
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Will we be required to know the expectation and variance for some standard 
random variables?

-Andrew
----------------------------
Illegitmitatum Non Carborundum Est
Andrew Shafer, MIT Blog
http://shaferandrew.blogspot.com
Si hoc legere scis numium eruditionis habes.
----------------------------


From ksindi@MIT.EDU Sun Dec  4 21:07:43 2005
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I found everything well explained but still have trouble grasping the
qualitative nature of the standard deviation; when to say, for example, "wow
that's a high standard deviation!". I'd also like to hear in class, in a bit
more detail, about when one can't use the Chebychev inequality.

-Kamil

From bens@MIT.EDU Mon Dec  5 01:42:24 2005
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The Chebyshev bound is awesome.
Are there distributions for which Markov, chebyshev, etc. are always tight?

From kjhollen@MIT.EDU Mon Dec  5 03:01:13 2005
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Subject: [Hanson] confused about calculating variance
From: Kate Hollenbach <kjhollen@MIT.EDU>
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while the gambling game example for calculating variance in the notes
was a good illustrating of the concept (p. 13), I found it difficult to
apply the concept to the online tutor problem about the variance of the
number of heads that appear in 100 coin flips. it might be helpful to
explain how to do variance calculations with an example that has more
than 2 outcomes.


From meyer@imap.theory.csail.mit.edu  Mon Dec 05 09:33:26 2005
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did you read ln14, sec 1.4?
regards, A.

Kate Hollenbach wrote:

>while the gambling game example for calculating variance in the notes
>was a good illustrating of the concept (p. 13), I found it difficult to
>apply the concept to the online tutor problem about the variance of the
>number of heads that appear in 100 coin flips. it might be helpful to
>explain how to do variance calculations with an example that has more
>than 2 outcomes.
>
>  
>


From xiaoranz@MIT.EDU Mon Dec  5 10:48:37 2005
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From: "Xiaoran (Sharon) Zhang" <xiaoranz@MIT.EDU>
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X-Keywords: NonJunk NotJunk                                                                                   


I thought section 2.1 on expected number of meals to get each Racin' Rocket is
an interesting problem and application. Also, The Markov and Chebyshev's
Theorems provides good way to upper bound certain probabilities and helpful in
checking result.

Xiaoran Zhang

**************************************************
Xiaoran (Sharon) Zhang
Class of 2008
Department of Biology &
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
320 Memorial Drive,
Cambridge, MA 02139
E-mail: xiaoranz@mit.edu
**************************************************

From meyer@imap.theory.csail.mit.edu  Mon Dec 05 10:32:54 2005
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yes, and it did help me figure out how to find the expectation of the 
number of
heads. but I am still having trouble with translating it into the variance
formulas found on p. 13 and 16.

Quoting "Prof. Albert R. Meyer" <meyer@csail.mit.edu>:

> did you read ln14, sec 1.4?
> regards, A.
>
> Kate Hollenbach wrote:
>
>> while the gambling game example for calculating variance in the notes
>> was a good illustrating of the concept (p. 13), I found it difficult to
>> apply the concept to the online tutor problem about the variance of the
>> number of heads that appear in 100 coin flips. it might be helpful to
>> explain how to do variance calculations with an example that has more
>> than 2 outcomes.
>>
>>
>


From hmzhou@blackbird.csail.mit.edu Mon Dec  5 11:15:57 2005
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From: Hanson Zhou <hmzhou@theory.csail.mit.edu>
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X-Keywords: NotJunk                                                                                           

Well...Chebyshev is nothing more than applying Markov's inequality in a
certain way as you will see from the proof of it, so I don't think you
can say it is stronger.  However, typically, if the variance is known to
you and small, then you can often do better using that than the usual
form of Markov.

-Hanson

On Sun, 4 Dec 2005, Kevin Wang wrote:

> Hi,
>
> I had a general question about this week's readings--it seems that
> Chebyshev's Inequality (S7, Pg. 12) is always tighter than Markov's
> Inequality (S6, pg. 10). Is there a simple proof of this, or is it even true?
>
> Thanks,
> Kevin
>
>

From meyer@imap.theory.csail.mit.edu  Mon Dec 05 18:34:20 2005
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Richard,

You also know that if A and B are independent, then Var(A+B) = Var(A) + 
Var(B). 

Or, if A1, A2, ..., A99, A100 are mutually independent, then 
Var(A1+A2+...+A99+A100) = Var(A1)+Var(A2)+...+Var(A99)+Var(A100).

DS

Richard Hughes wrote:

> Or, I can figure out how, but I can't actually DO it.
>
> I'm trying to do Tutor Problem 14.3 and it's giving me more trouble 
> than a tutor problem should.  I honest to god cannot figure out how to 
> calculate the variance on a number of heads.  I know the formula is 
> Var(R) = E[R^2] - E^2[R].  I know that E^2[R] = 2500.  I absolutely 
> cannot figure out how to calculate R^2 or the expected value of it.  
> HELP ME PLEASE. :-(
>
> Richard
>

From meyer@imap.theory.csail.mit.edu  Mon Dec 05 18:41:40 2005
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Subject: Re: I can't figure out how to calculate variance
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X-Keywords: NotJunk                                                                                            

Thanks.  That did it for me, I solved it.

Richard

On Dec 5, 2005, at 6:34 PM, David Shin wrote:

> Richard,
>
> You also know that if A and B are independent, then Var(A+B) = Var(A) 
> + Var(B).
> Or, if A1, A2, ..., A99, A100 are mutually independent, then 
> Var(A1+A2+...+A99+A100) = Var(A1)+Var(A2)+...+Var(A99)+Var(A100).
>
> DS
>
> Richard Hughes wrote:
>
>> Or, I can figure out how, but I can't actually DO it.
>>
>> I'm trying to do Tutor Problem 14.3 and it's giving me more trouble 
>> than a tutor problem should.  I honest to god cannot figure out how 
>> to calculate the variance on a number of heads.  I know the formula 
>> is Var(R) = E[R^2] - E^2[R].  I know that E^2[R] = 2500.  I 
>> absolutely cannot figure out how to calculate R^2 or the expected 
>> value of it.  HELP ME PLEASE. :-(
>>
>> Richard
>>

From meyer@imap.theory.csail.mit.edu  Mon Dec 05 18:41:38 2005
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Subject: Re: I can't figure out how to calculate variance
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X-Keywords: NonJunk NotJunk                                                                                            

Thanks.  That did it for me, I solved it.

Richard

On Dec 5, 2005, at 6:34 PM, David Shin wrote:

> Richard,
>
> You also know that if A and B are independent, then Var(A+B) = Var(A) 
> + Var(B).
> Or, if A1, A2, ..., A99, A100 are mutually independent, then 
> Var(A1+A2+...+A99+A100) = Var(A1)+Var(A2)+...+Var(A99)+Var(A100).
>
> DS
>
> Richard Hughes wrote:
>
>> Or, I can figure out how, but I can't actually DO it.
>>
>> I'm trying to do Tutor Problem 14.3 and it's giving me more trouble 
>> than a tutor problem should.  I honest to god cannot figure out how 
>> to calculate the variance on a number of heads.  I know the formula 
>> is Var(R) = E[R^2] - E^2[R].  I know that E^2[R] = 2500.  I 
>> absolutely cannot figure out how to calculate R^2 or the expected 
>> value of it.  HELP ME PLEASE. :-(
>>
>> Richard
>>


From cwong08@MIT.EDU Mon Dec  5 20:29:29 2005
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I would like clarification on when to use standard deviation and when to 
use variance.(pgs 14-16)


From icharny@MIT.EDU Mon Dec  5 21:26:20 2005
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I don't really understand variance, what it is, and how to quickly 
calculate it. Please cover this in the next lecture.

~Isaac


From vixen@MIT.EDU Mon Dec  5 23:40:24 2005
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I was confused as to what "x" was in many of the equations.

From hkhall@MIT.EDU Mon Dec  5 23:48:48 2005
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From: Harrison King Hall <hkhall@MIT.EDU>
Subject: [David] Week 14 Comments
Date: Mon, 5 Dec 2005 23:48:37 -0500
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David-
Even after our talk today I still had trouble with variance and using 
it to apply Chebyshev's theorem.  For that matter is is taking me a 
long time to figure out Markov's as well.  I think that I will get it 
by the time we go over it in class.  I think that my problem lies in 
the fact that I don't really get E[R-c] where R is a random variable 
and c is some constant.  I think this can be explained by linearity of 
expectation and E[c]=c, but I am not sure.
Well, that is all I have to say, but I need to tell you that there is a 
high probability that I will not be in class on Friday since I have an 
away swim meet.  I will get back to you on the exact time that I will 
be leaving and whether it means I will miss class.
-Harrison


From medrano@MIT.EDU Tue Dec  6 03:16:13 2005
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Subject: [sayan] final weekly question. finally
From: Jesus Medrano <medrano@MIT.EDU>
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Hi Sayan,

My question is: is the Markov Theorem actually used in practice? It does
not seem to be a great bound.  The last problem in the tutor problem to
be true.  Also I kept submitting the same answer it kept telling me it
was wrong and eventually it said it was right.  I had to kept with a
friend to make sure I was doing it right and he submitted the same
answer I did and it said it was wrong.  So my question again is 
Why Markov? Why?!

Jesus Medrano


From lye@MIT.EDU Tue Dec  6 09:54:05 2005
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X-Keywords: NotJunk                                                                               

Is there a general way of calculating E[X^2] (like a nice compact formula)? What
about for continuous distributions?

From cbossard@MIT.EDU Tue Dec  6 12:00:50 2005
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I am a little confused about Chebyshev's inequality in terms of
standard deviation.

Also on the TP I dont understand why to get 6,000 more than the other
candidate you don't just need half + 6,000 but it keeps telling me
that 3,006,000 is wrong.

Cynthia

From rnjacobs@MIT.EDU Tue Dec  6 13:54:29 2005
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X-Keywords: NonJunk NotJunk                                                                                   


Nope, nothing new in the reading.

 - Robert Jacobs

From meyer@imap.theory.csail.mit.edu  Tue Dec 06 15:28:38 2005
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Date: Tue, 06 Dec 2005 15:28:46 -0500
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To: Jesus Medrano <medrano@MIT.EDU>
Subject: Re: [sayan] final weekly question. finally
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Tutor behaved properly for me and answers are correct.  Which part of 
the last problem did you find the tutor to be responding inconsistently on?

Anyway, Markov rarely gives a good bound all by itself, but can be 
powerful when combined with other tricks.  For example, by applying 
Markov to (R-m)^2, where m = E[R], we derive the Chebyshev bound about R.

regards, A.


Jesus Medrano wrote:
> Hi Sayan,
> 
> My question is: is the Markov Theorem actually used in practice? It does
> not seem to be a great bound.  The last problem in the tutor problem to
> be true.  Also I kept submitting the same answer it kept telling me it
> was wrong and eventually it said it was right.  I had to kept with a
> friend to make sure I was doing it right and he submitted the same
> answer I did and it said it was wrong.  So my question again is 
> Why Markov? Why?!
> 
> Jesus Medrano
> 


From meyer@imap.theory.csail.mit.edu  Tue Dec 06 15:31:01 2005
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The total # of votes is fixed, so if one candidate got 6000 over the 
mean, the other would have to get 6000 BELOW the mean, so the diff would 
be 12,000.

regards, A.

cbossard@MIT.EDU wrote:
> I am a little confused about Chebyshev's inequality in terms of
> standard deviation.
> 
> Also on the TP I dont understand why to get 6,000 more than the other
> candidate you don't just need half + 6,000 but it keeps telling me
> that 3,006,000 is wrong.
> 
> Cynthia


From meyer@imap.theory.csail.mit.edu  Tue Dec 06 15:37:36 2005
Message-ID: <4395F698.8010402@csail.mit.edu>
Date: Tue, 06 Dec 2005 15:37:44 -0500
From: "Prof. Albert R. Meyer" <meyer@csail.mit.edu>
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To: Linda Ye <lye@MIT.EDU>
Subject: Re: [Sayan] Week 14 Comments
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We don't do continuous distributions in discrete Math :-), though 
replacing our sums by integrals (of suitable kind) often allows discrete 
results to carry over to the continuous case.

And no, there's no closed form formula for E[X^2] in terms of say, E[X]. 
  But for the kind of simple X's we consider, it's usually possible to 
calculate E[X^2] from first principles using the def and linearity of E[].

regards, A

Linda Ye wrote:
> Is there a general way of calculating E[X^2] (like a nice compact formula)? What
> about for continuous distributions?


From meyer@imap.theory.csail.mit.edu  Tue Dec 06 16:09:46 2005
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Subject: Re: [sayan] final weekly question. finally
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Part 3 of the last problem.  I put 1000/1001 and 1000 / 1001 and many
combinations with different spacing and I received the x mark on all of
them.  Then out of nowhere it worked. 

Jesus

On Tue, 2005-12-06 at 15:28 -0500, Prof. Albert R. Meyer wrote:
> Tutor behaved properly for me and answers are correct.  Which part of 
> the last problem did you find the tutor to be responding inconsistently on?
> 
> Anyway, Markov rarely gives a good bound all by itself, but can be 
> powerful when combined with other tricks.  For example, by applying 
> Markov to (R-m)^2, where m = E[R], we derive the Chebyshev bound about R.
> 
> regards, A.
> 
> 
> Jesus Medrano wrote:
> > Hi Sayan,
> > 
> > My question is: is the Markov Theorem actually used in practice? It does
> > not seem to be a great bound.  The last problem in the tutor problem to
> > be true.  Also I kept submitting the same answer it kept telling me it
> > was wrong and eventually it said it was right.  I had to kept with a
> > friend to make sure I was doing it right and he submitted the same
> > answer I did and it said it was wrong.  So my question again is 
> > Why Markov? Why?!
> > 
> > Jesus Medrano
> > 

From meyer@imap.theory.csail.mit.edu  Tue Dec 06 16:59:11 2005
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Date: Tue, 06 Dec 2005 16:59:11 -0500
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Subject: Re: [sayan] final weekly question. finally
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Ok, I'm cc'ing the tutor-master to watch for similar probs.  thx for the 
info.
regards, A.

Jesus Medrano wrote:

>Part 3 of the last problem.  I put 1000/1001 and 1000 / 1001 and many
>combinations with different spacing and I received the x mark on all of
>them.  Then out of nowhere it worked. 
>
>Jesus
>
>On Tue, 2005-12-06 at 15:28 -0500, Prof. Albert R. Meyer wrote:
>  
>
>>Tutor behaved properly for me and answers are correct.  Which part of 
>>the last problem did you find the tutor to be responding inconsistently on?
>>
>>Anyway, Markov rarely gives a good bound all by itself, but can be 
>>powerful when combined with other tricks.  For example, by applying 
>>Markov to (R-m)^2, where m = E[R], we derive the Chebyshev bound about R.
>>
>>regards, A.
>>
>>
>>Jesus Medrano wrote:
>>    
>>
>>>Hi Sayan,
>>>
>>>My question is: is the Markov Theorem actually used in practice? It does
>>>not seem to be a great bound.  The last problem in the tutor problem to
>>>be true.  Also I kept submitting the same answer it kept telling me it
>>>was wrong and eventually it said it was right.  I had to kept with a
>>>friend to make sure I was doing it right and he submitted the same
>>>answer I did and it said it was wrong.  So my question again is 
>>>Why Markov? Why?!
>>>
>>>Jesus Medrano
>>>
>>>      
>>>


From hectorb@MIT.EDU Tue Dec  6 18:25:01 2005
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"High variance is often associated with high risk. For example, in ten 
rounds of Game A,
we expect to make $10, but could conceivably lose $10 instead. On the 
other hand, in ten
rounds of game B, we also expect to make $10, but could actually lose 
more than $20,000!" -p.14

I found this example interesting. I enjoy reading the examples that 
involve gambling.

-Hector

From dowgun@MIT.EDU Tue Dec  6 18:42:15 2005
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From: Neil M Dowgun <dowgun@MIT.EDU>
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I feel like I must be misunderstanding variance, because it seems incredibly
difficult to calculate for problems that don't have only two outcomes (say, the
third problem on the tutor problems). I mean, if you flip a fair coin 100 times,
then to get E(A - E(A))^2 you have to calculate the probability of each and
every value of A coming up, right? And then add them all up? There has to be an
easier way to do this.
Thanks, Neil

From meyer@imap.theory.csail.mit.edu  Tue Dec 06 20:57:01 2005
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the variance is 100 times the variance of one flip (which is1/2(1-1/2) = 
1/4) so the answer is 100(1/4)=25; see Notes 14, section 8.3.

Neil M Dowgun wrote:

>I feel like I must be misunderstanding variance, because it seems incredibly
>difficult to calculate for problems that don't have only two outcomes (say, the
>third problem on the tutor problems). I mean, if you flip a fair coin 100 times,
>then to get E(A - E(A))^2 you have to calculate the probability of each and
>every value of A coming up, right? And then add them all up? There has to be an
>easier way to do this.
>Thanks, Neil
>  
>


From petek@MIT.EDU Tue Dec  6 21:50:05 2005
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Subject: [Jelani] Markov+ Chebsnsvlskjdfslky
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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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<head>
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<font size="-1">Despite the fact that my eyes / brain refuse to take
the effort to actually pronounce the second man's name, I got through
this week's notes in an ok manner.&nbsp; I'm having trouble getting the last
TP prob right.&nbsp; Given standard dev = 2000, and you want the pr that
gore gets 6,000 more votes, shouldn't the prob be bounded by (standard
dev)^2 / (6,000)^2&nbsp; =&nbsp; 1/9?&nbsp; <br>
<br>
Also, this week's notes is a good example of stuff that rather than
make a good home in my head will probably make a much better one on the
crib sheet we bring into the exam.<br>
<br>
-P<br>
</font>
<pre class="moz-signature" cols="72">-- 
Pete Kruskall
28 The Fenway
Boston, MA 02215

:::::::::::::::::::::::::::::
508.843.5861 ::::Cell Phone::
617.536.9925 :::Sigma Nu:::::</pre>
</body>
</html>

From mracich@MIT.EDU Tue Dec  6 23:35:39 2005
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Subject: [Sayan] Comments for Course Notes, Week 14 (Missed Expectations?)
From: Moira Racich <mracich@MIT.EDU>
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I found all of section 7, "Chebyshev's Theorem", (starting on page 12)
incredibly confusing.  I also had a very hard time with the tutor
problem questions that related to this material.  I would appreciate it
if this section was reviewed in depth.  

Thanks, 
Moira Racich


From rian@MIT.EDU Tue Dec  6 23:40:50 2005
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To: 6042-probs@theory.lcs.mit.edu
From: Rian Hunter <rian@MIT.EDU>
Subject: [jelani] - week 14 comments
Date: Tue, 6 Dec 2005 23:40:45 -0500
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This week's reading is very long, lots of concepts to learn and/or  
equations to memorize. I'd like to request that when the class is  
tested on this material, it isn't very very rigorous since to  
adequately absorb this material and apply it, it will take longer  
than a week or at least I think so.

Variance and Chebyshev's Theorem seem clear and sensible to me but in  
class I would like to see more applications of these tools to gain a  
better grasp of the topics. Also way more examples of ways to  
calculate variance in different circumstances or problems.

Rian Hunter

From ridell@MIT.EDU Tue Dec  6 23:42:31 2005
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From: Rebecca Idell <ridell@MIT.EDU>
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Subject: tp 14 reading comments [hanson]
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Can you give more examples of problems with Markov's thm from p10 of the
reading?
-Rebecca Idell

-- 
Rebecca Idell
Massachusetts Institute of Technology
Electrical Engineering and Computer Science, Class of 2007

479 Commonwealth Avenue
Boston, MA 02215
(617) 875-0889

From tonyng@MIT.EDU Tue Dec  6 23:57:30 2005
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To: 6042-probs@theory.lcs.mit.edu
From: Tony Ng <tonyng@MIT.EDU>
Subject: [Jelani] Reading Comments Week 14
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I have a question on theorem 4.2 on pg8. Given two independent random 
variables A and B, E[A*B] = E[A]*E[B]. However, is it true backwards? Does 
E[A]*E[B] = E[A*B] imply that A and B are independent?

- Tony Ng


From ereid@MIT.EDU Wed Dec  7 00:07:53 2005
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To: 6042-probs@theory.lcs.mit.edu
From: Elizabeth Reid <ereid@MIT.EDU>
Subject: [Hanson] Reading Comment
Date: Wed, 7 Dec 2005 00:07:43 -0500
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The definition used for Variance and Standard Deviation seem like  
overly complicated ones. Maybe it's just the shorthand used, but it's  
a lot less intuitive this way than other ways that I have learned it.

From jehan@MIT.EDU Wed Dec  7 00:40:01 2005
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Subject: [Sayan] Week 14 Comments
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No questions.
I thought Markov's approximation was pretty cool.

jehan


From avalys@MIT.EDU Wed Dec  7 00:42:53 2005
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To: 6042-probs@theory.lcs.mit.edu
From: Alex Valys <avalys@MIT.EDU>
Subject: 6.042: [Hanson] Required Reading Comments
Date: Wed, 7 Dec 2005 00:42:51 -0500
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I am a little unclear about how to use the alternative expression of  
variance described in section 8.2 (page 8).  How is one supposed to  
find the value of E[R^2]?

Alex


From yangc@MIT.EDU Wed Dec  7 00:43:45 2005
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From: "Chris Yang" <yangc@MIT.EDU>
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Subject: [David] Week 14 Comments
Date: Wed, 7 Dec 2005 00:43:37 -0500
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Oh my God, I don't understand Variance or the two theorems about the
upper/lower bounds at all.  Please tell me we are going over this in class.

 

Thanks,

Chris Yang


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<p class=3DMsoNormal><font size=3D2 face=3DArial><span =
style=3D'font-size:10.0pt;
font-family:Arial'>Oh my God, I don&#8217;t understand Variance or the =
two
theorems about the upper/lower bounds at all.&nbsp; Please tell me we =
are going over
this in class.<o:p></o:p></span></font></p>

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From hzhou@MIT.EDU Wed Dec  7 00:46:07 2005
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Date: Wed, 07 Dec 2005 00:46:21 -0500
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Subject: [David] tp-14
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Hi,

Topic is clear.  I have a question for problem set number 5.  With the 
answer i got for (a), if i were to solve for K that minimized that 
expression, I get a quadratic equation.  Is it that complicated, or did I 
do part (a) wrong. Thanks.

- Steven Zhou


From mukkala@MIT.EDU Wed Dec  7 01:00:06 2005
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From: Praveen Pamidimukkala <mukkala@MIT.EDU>
Subject: [Sayan] Week 14 Comments
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Hi,

My question pertains to the TP as opposed to the reading.  In TP.14.3, it 
reads:


"Suppose you flip a fair coin 100 times. The coin flips are all mutually 
independent.

3. What is the variance of the number of heads?

Let Xi be the random variable whose value is 1 if the ith coin flip is 
heads. Then
Var[Xi] = 1/2 - (1/2)2 = 1/4. So Var[X1 + ... + X100] = 100·(1/4) = 25.
(Note we can use additivity of variance here since our flips are 
independent.) ] "

I don't understand the calculation for Var[Xi].

Thanks,
Praveen 



From dnreshef@MIT.EDU Wed Dec  7 01:05:43 2005
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This week's reading was particularly technical and cumbersome.  I had the most
trouble understanding condeitional expectation and variance.  Could we please
go over variance real quickly in class, I know it's not a really hard concept,
I just don't really understand it quite yet.
-Dave

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The topic of standard deviation was a little bit confusing but everything else
was very clear.

From ctsims@MIT.EDU Wed Dec  7 01:23:21 2005
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From: Clayton Sims <ctsims@MIT.EDU>
Subject: [6.042] Reading Comment Lecture Notes 14 - Clayton Sims
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I found that the definition of Variance using a the expectation of (random 
variable minus it's expected value) squared) defined on page 13, and used 
in the next 3 or 4 pages, to be hard to apply to some real world cases. It 
would be nice to have it more fully explained in class.

-Clayton 


From jjmonzon@MIT.EDU Wed Dec  7 01:24:33 2005
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From: "Joshua Jen C. Monzon" <jjmonzon@MIT.EDU>
Subject: [David] - Reading Comments
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I liked the section about inequalities (Markov and Chebyshev) - they 
are very great at giving bounds. I am just wondering if Central Limit 
Theorem is going to be introduced in this class.

Josh



Joshua Jen C. Monzon
Massachusetts Institute of Technology
Electrical Engineering with Computer Science
jjmonzon@mit.edu   617-803-7497


From pgroudas@MIT.EDU Wed Dec  7 01:37:45 2005
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From: Paul Groudas <pgroudas@MIT.EDU>
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Subject: [HANSON] Week 14 reading comments
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I thought the reading was okay, but shouldn't there have been a section on
binomial standard deviation?  Was this just covered already or something?  I
needed to look it up on the web so i could get the variance for one of the
tutor problems.

-Paul

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From: Shreyes Seshasai <shreyes@mit.edu>
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Hi,

Comments on the reading for this week:

My comments deal with the two inequalities presented, Markov's (on page 10)
and Chebyshev's (on page 12).  The calculations required for both of these
make sense to me and don't seem too difficult, but one thing I'd like to se=
e
discussed in class is the types of problems that each of these can help
resolve, and suggestions on how to decide which inequality to choose when
given a problem.  I know Markov's inequality deals with the values greater
than a specified number, but could Chebyshev's be used as well with careful
selection of x in the inequality.  (Hopefully the answer to this is better
than the one given in 6.041 this morning, that Chebyshev's has an x^2 in th=
e
denominator, so when given a bound of that form, use that inequality).


Thanks,
Shreyes

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Hi,<br>
<br>
Comments on the reading for this week:<br>
<br>
My comments deal with the two inequalities presented, Markov's (on page
10) and Chebyshev's (on page 12).&nbsp; The calculations required for
both of these make sense to me and don't seem too difficult, but one
thing I'd like to see discussed in class is the types of problems that
each of these can help resolve, and suggestions on how to decide which
inequality to choose when given a problem.&nbsp; I know Markov's
inequality deals with the values greater than a specified number, but
could Chebyshev's be used as well with careful selection of x in the
inequality.&nbsp; (Hopefully the answer to this is better than the one
given in 6.041 this morning, that Chebyshev's has an x^2 in the
denominator, so when given a bound of that form, use that inequality).<br>
<br>
<br>
Thanks,<br>
Shreyes<br>

------=_Part_13968_10334097.1133937794310--

From jeffhoff@MIT.EDU Wed Dec  7 02:08:26 2005
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Page 13
Section 7.0

Variance!  I don't get it.  I can't calculate it.
The binomial example didn't really help much.
Can we get a better example with real numbers?


From mpapi@MIT.EDU Wed Dec  7 03:04:56 2005
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Subject: [Jelani] week 14 comments
From: Matt Papi <mpapi@MIT.EDU>
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Section 7, on Chebyshev's theorem; I thought that the applications of
Chebyshev's theorem (i.e. in the tutor problem) were pretty hard to
grasp. I also don't really understand how it is derived from Markov's
theorem.

- Matt


From mwangi@MIT.EDU Wed Dec  7 03:08:35 2005
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Passage:  7  Chebyshev's Theorem
Page:       12
I found this passage most  surprising because of the very low probability 
of a random variable deviating more than a few standard deviations from the 
mean. I found it especially surprising because Chebyshev's Theorem can be 
applied to any random variable, regardless of what it's PDF is.

Sincerely,
Timothy M. Mwangi 


From meyer@imap.theory.csail.mit.edu  Wed Dec 07 03:22:16 2005
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Date: Wed, 07 Dec 2005 03:22:17 -0500
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To: Pete Kruskall <petek@MIT.EDU>
Subject: Re: [Jelani] Markov+ Chebsnsvlskjdfslky
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if Gore got 6000 more votes, then he got 3000 ABOVE THE MEAN, and Bush got
3000 below, so the denominator should be (3000)^2.
 
Regards, A.
 
Pete Kruskall wrote:
 
 > Despite the fact that my eyes / brain refuse to take the effort to 
actually pronounce the second man's name, I got through this week's 
notes in an ok manner.  I'm having trouble getting the last TP prob 
right.  Given standard dev = 2000, and you want the pr that gore gets 
6,000 more votes, shouldn't the prob be bounded by (standard dev)^2 / 
(6,000)^2  =  1/9?  
 >
 > Also, this week's notes is a good example of stuff that rather than 
make a good home in my head will probably make a much better one on the 
crib sheet we bring into the exam.
 >
 > -P
 >
 >--  
 >Pete Kruskall
 >28 The Fenway
 >Boston, MA 02215
 >
 >:::::::::::::::::::::::::::::
 >508.843.5861 ::::Cell Phone::
 >617.536.9925 :::Sigma Nu:::::
 >



From lana@MIT.EDU Wed Dec  7 03:41:14 2005
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From: Svetlana Goldenberg <lana@MIT.EDU>
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Subject: [Sayan] TP14
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The part that I found to be the most intriguing was the symmetry in the
alternative definition of variance, particularly because the link between the
original definition and this version is far from intuitive.
Lana

From lkini@MIT.EDU Wed Dec  7 03:52:19 2005
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Hi Hanson,

I found Chebyshev's Theorem most interesting cause it gives limits in 
the broadest of applications: from the birthday problem to the IQ example.

Lohith

From meyer@imap.theory.csail.mit.edu  Wed Dec 07 04:12:18 2005
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What was your intuitive way?
regards, A.

Elizabeth Reid wrote:

> The definition used for Variance and Standard Deviation seem like  
> overly complicated ones. Maybe it's just the shorthand used, but it's  
> a lot less intuitive this way than other ways that I have learned it.



From meyer@imap.theory.csail.mit.edu  Wed Dec 07 04:16:03 2005
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Central lim thm will be mentioned VERY briefly next week, along with an 
explanation of why it's not of central interest in engineering and 
computer science.

regards,

Joshua Jen C. Monzon wrote:

> I liked the section about inequalities (Markov and Chebyshev) - they 
> are very great at giving bounds. I am just wondering if Central Limit 
> Theorem is going to be introduced in this class.
>
> Josh
>
>
>
> Joshua Jen C. Monzon
> Massachusetts Institute of Technology
> Electrical Engineering with Computer Science
> jjmonzon@mit.edu   617-803-7497
>


From fluff@MIT.EDU Wed Dec  7 04:39:23 2005
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I found all the examples about IQ really depressing. They just seem  
to highlight the fact that I don't understand something. I still  
don't intuitively get how you can just ignore dependence/independence  
for the linearity of expectation stuff.

~Crystal

From benlu@MIT.EDU Wed Dec  7 08:09:11 2005
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Hey Jelani,

It's cool to finally have a definition for variance and standard 
deviation! I've heard those terms thrown around so many times, and until 
now have had to rely on a purely intuitive understanding of what people 
meant. As for things that I don't understand, I mostly just need 
practice calculating those E(R^2) terms.

Cheers,
~Ben Lu





From kromer@MIT.EDU Wed Dec  7 10:01:41 2005
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X-Keywords: NonJunk NotJunk                                                                                   

"the expected value of a sum of random variables is the sum of the
expected values of the variables" (p. 1)

When we consider two random variables at the same time, I understand how
we get the expectation of their sum, but how do we expect the values of
the variables to be distributed? For example, say we're rolling two
six-sided dice. We expect them to sum to 7, and each die individualy has
an expected value of 3.5. Is the expected maximum of the dice 3.5,
because that's the expectation of each die? It seems likely that one die
would have a higher value than the other on most rolls, so it seems that
the expected maximum must be larger than 3.5 and the expected minimum
must be less--how would you go about computing this?

Katherine

From arup@MIT.EDU Wed Dec  7 10:09:07 2005
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The coupon collector problem (section 2) is probably the most 
interesting section in this week's reading to me because I've seen most 
of the rest of it in my statistics class, I think.  Most of it seemed 
pretty straightforward.

|Arup|

From ryoung@MIT.EDU Wed Dec  7 10:40:24 2005
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I had a really hard time with Chebyshev bounds, and couldn't even get 
them on the tutor problems for the longest time.  I understand them 
now, but it took me a while.

-Ryan


From rshroff@MIT.EDU Wed Dec  7 10:43:24 2005
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I found the properties of expectation and variance of trhe various distributions
extremely interesting.

-Rahul Shroff

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From: Zachary Adam Ozer <zozer@mit.edu>
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I though that both of these sections of the notes were really well
done. Having read them, I really felt like I understood the concepts
and I'm eager to tackle the in class problems on them. But really, I
just wanted to compliment y'all on an excellent job.

-zozer


From fgreen@MIT.EDU Wed Dec  7 10:53:22 2005
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Date: Wed,  7 Dec 2005 10:53:13 -0500
From: Forrest O Green <fgreen@MIT.EDU>
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Subject: julani, required reading comment 14, from Forrest Green
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I was really glad to finally figure out where variences and standerd deviations
came from.

    -Forrest

From yaser@MIT.EDU Wed Dec  7 11:03:46 2005
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From: "Yaser Khan" <yaser@MIT.EDU>
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Subject: David: tp14 reading comments
Date: Wed, 7 Dec 2005 11:03:23 -0500
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Hi David,
 
On a small point, could we use Markov instead of Chebyshev's theorem in
cases where the distribution is symmetric? (As I understand that it shows
the 'distance' from the mean in either direction as opposed to Markov who
simply shows the deviation 'above the mean'.) 
 
My actual comment is that the proof of variance of sums seems really
intimidating, I hope that the lecture & lecture slides can clear up what
exactly needs to be understood about its properties.
 
Thanks!
 
_Yaser

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<p class=3DMsoNormal><font size=3D2 color=3Dnavy face=3DVerdana><span =
style=3D'font-size:
10.0pt;font-family:Verdana;color:navy'>Hi =
David,<o:p></o:p></span></font></p>

<p class=3DMsoNormal><font size=3D2 color=3Dnavy face=3DVerdana><span =
style=3D'font-size:
10.0pt;font-family:Verdana;color:navy'><o:p>&nbsp;</o:p></span></font></p=
>

<p class=3DMsoNormal><font size=3D2 color=3Dnavy face=3DVerdana><span =
style=3D'font-size:
10.0pt;font-family:Verdana;color:navy'>On a small point, could we use =
Markov instead
of <span class=3DSpellE>Chebyshev&#8217;s</span> theorem in cases where =
the
distribution is symmetric? (As I understand that it shows the =
&#8216;distance&#8217;
from the mean in either direction as opposed to Markov who simply shows =
the
deviation &#8216;above the mean&#8217;.) <o:p></o:p></span></font></p>

<p class=3DMsoNormal><font size=3D2 color=3Dnavy face=3DVerdana><span =
style=3D'font-size:
10.0pt;font-family:Verdana;color:navy'><o:p>&nbsp;</o:p></span></font></p=
>

<p class=3DMsoNormal><font size=3D2 color=3Dnavy face=3DVerdana><span =
style=3D'font-size:
10.0pt;font-family:Verdana;color:navy'>My actual comment is that the =
proof of
variance of sums seems really <span class=3DGramE>intimidating,</span> I =
hope
that the lecture &amp; lecture slides can clear up what exactly needs to =
be
understood about its properties.<o:p></o:p></span></font></p>

<p class=3DMsoNormal><font size=3D2 color=3Dnavy face=3DVerdana><span =
style=3D'font-size:
10.0pt;font-family:Verdana;color:navy'><o:p>&nbsp;</o:p></span></font></p=
>

<p class=3DMsoNormal><font size=3D2 color=3Dnavy face=3DVerdana><span =
style=3D'font-size:
10.0pt;font-family:Verdana;color:navy'>Thanks!<o:p></o:p></span></font></=
p>

<p class=3DMsoNormal><font size=3D2 color=3Dnavy face=3DVerdana><span =
style=3D'font-size:
10.0pt;font-family:Verdana;color:navy'><o:p>&nbsp;</o:p></span></font></p=
>

<p class=3DMsoNormal><font size=3D2 color=3Dnavy face=3DVerdana><span =
style=3D'font-size:
10.0pt;font-family:Verdana;color:navy'>_Yaser<o:p></o:p></span></font></p=
>

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From mitras@theory.csail.mit.edu Wed Dec  7 11:06:22 2005
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Date: Wed, 07 Dec 2005 11:06:12 -0500
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To: Andrew Shafer <ajshafer@MIT.EDU>
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Subject: Re: [Sayan] Week 14 Comments
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Yes.
-sayan
Andrew Shafer wrote:

> Will we be required to know the expectation and variance for some 
> standard random variables?
>
> -Andrew
> ----------------------------
> Illegitmitatum Non Carborundum Est
> Andrew Shafer, MIT Blog
> http://shaferandrew.blogspot.com
> Si hoc legere scis numium eruditionis habes.
> ----------------------------
>


From meyer@imap.theory.csail.mit.edu  Wed Dec 07 08:03:28 2005
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Date: Wed, 07 Dec 2005 08:03:40 -0500
From: Pete Kruskall <petek@MIT.EDU>
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To: "Prof. Albert R. Meyer" <meyer@csail.mit.edu>
Subject: Re: [Jelani] Markov+ Chebsnsvlskjdfslky
References: <43964DE4.2020005@mit.edu> <43969BB9.2040402@csail.mit.edu>
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<font size="-1">Haha.&nbsp;&nbsp; That would make more sense.&nbsp; Thanks!<br>
<br>
-P<br>
</font><br>
Prof. Albert R. Meyer wrote:
<blockquote cite="mid43969BB9.2040402@csail.mit.edu" type="cite">if
Gore got 6000 more votes, then he got 3000 ABOVE THE MEAN, and Bush got
  <br>
3000 below, so the denominator should be (3000)^2.
  <br>
  <br>
Regards, A.
  <br>
  <br>
Pete Kruskall wrote:
  <br>
  <br>
&gt; Despite the fact that my eyes / brain refuse to take the effort to
actually pronounce the second man's name, I got through this week's
notes in an ok manner.&nbsp; I'm having trouble getting the last TP prob
right.&nbsp; Given standard dev = 2000, and you want the pr that gore gets
6,000 more votes, shouldn't the prob be bounded by (standard dev)^2 /
(6,000)^2&nbsp; =&nbsp; 1/9?&nbsp; &gt;
  <br>
&gt; Also, this week's notes is a good example of stuff that rather
than make a good home in my head will probably make a much better one
on the crib sheet we bring into the exam.
  <br>
&gt;
  <br>
&gt; -P
  <br>
&gt;
  <br>
&gt;--&nbsp; &gt;Pete Kruskall
  <br>
&gt;28 The Fenway
  <br>
&gt;Boston, MA 02215
  <br>
&gt;
  <br>
&gt;:::::::::::::::::::::::::::::
  <br>
&gt;508.843.5861 ::::Cell Phone::
  <br>
&gt;617.536.9925 :::Sigma Nu:::::
  <br>
&gt;
  <br>
  <br>
</blockquote>
<br>
<pre class="moz-signature" cols="72">-- 
Pete Kruskall
28 The Fenway
Boston, MA 02215

:::::::::::::::::::::::::::::
508.843.5861 ::::Cell Phone::
617.536.9925 :::Sigma Nu:::::</pre>
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From hmzhou@blackbird.csail.mit.edu Wed Dec  7 11:12:09 2005
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From: Hanson Zhou <hmzhou@theory.csail.mit.edu>
To: Alex Valys <avalys@MIT.EDU>
cc: 6042-probs@theory.lcs.mit.edu
Subject: Re: 6.042: [Hanson] Required Reading Comments
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It shows you an example on page 9...you simply use definition of
expectation.

-Hanson

On Wed, 7 Dec 2005, Alex Valys wrote:

> I am a little unclear about how to use the alternative expression of
> variance described in section 8.2 (page 8).  How is one supposed to
> find the value of E[R^2]?
>
> Alex
>
>

From alisonc@MIT.EDU Wed Dec  7 11:13:35 2005
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I found the section on variance sort of confusing -- more examples would
have been really nice.

From dshin@MIT.EDU Wed Dec  7 11:13:46 2005
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There is no clean formula for E[max{A,B}] given E[A] and E[B].

One of the in-class problems actually outlined a nice way to compute 
E[max{k independent die}] - it used the formula

E[X] = \sum_i Pr[X>i]

DS

Katherine Romer wrote:

>"the expected value of a sum of random variables is the sum of the
>expected values of the variables" (p. 1)
>
>When we consider two random variables at the same time, I understand how
>we get the expectation of their sum, but how do we expect the values of
>the variables to be distributed? For example, say we're rolling two
>six-sided dice. We expect them to sum to 7, and each die individualy has
>an expected value of 3.5. Is the expected maximum of the dice 3.5,
>because that's the expectation of each die? It seems likely that one die
>would have a higher value than the other on most rolls, so it seems that
>the expected maximum must be larger than 3.5 and the expected minimum
>must be less--how would you go about computing this?
>
>Katherine
>  
>

From mitras@theory.csail.mit.edu Wed Dec  7 11:16:22 2005
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Hope you'll attend today's lecture.
An easy way to calculate variance is to use theorem 8.1, i.e., Var(X) = 
E(X^2) - (E(X))^2.
-sayan

Isaac Charny wrote:

> I don't really understand variance, what it is, and how to quickly 
> calculate it. Please cover this in the next lecture.
>
> ~Isaac
>


From crowell@MIT.EDU Wed Dec  7 11:17:08 2005
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I really found the examples (coupon collector etc) to be very interesting. 
These  were talked about in lecture and are on 1.3 on page 3.

-Rob Crowell




From kktyan@MIT.EDU Wed Dec  7 11:18:57 2005
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From: Karena Tyan <kktyan@MIT.EDU>
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I was a little confused over the definition of variance on page 13.

Var[R]::= E[(R - E[R])^2] makes it seem as if it's the expected value of the
squares of (R - E[R]), so I thought this meant calculating the sums of the
squares and dividing by the probability of each happening.  It seems to be more
like the square of the expected (R - E[R]) and then the expected value of
that...or something.  In other words, I'm confused over the definition of
variance.

- Karena

-- 
410 Memorial Drive
Cambridge, MA 02139
(585)957-5923

From dshin@MIT.EDU Wed Dec  7 11:27:25 2005
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Heh, Professor Meyer and I discussed this after class the other day.  
Were you able to solve it during the exam?

DS

Jason Juang wrote:

> I don't have much comment on the reading, so I'll instead mention that 
> Problem B4 on the Putnam today was, paraphrased:
>   Let f(m,n) be the number of n-tuples of integers (x_1, x_2, ..., 
> x_n) which satisfy |x_1| + |x_2| + ... + |x_n| <= m. Show that f(m,n) 
> = f(n,m).
>
> ... Sounds like a good final exam question to me ;)
>
> Jason.


From mitras@theory.csail.mit.edu Wed Dec  7 11:27:26 2005
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By alternative definition I guess you mean theorem 8.1, right ?
This also happens to be an easier definition to use when you are 
computing variance.
-Sayan

Svetlana Goldenberg wrote:

>The part that I found to be the most intriguing was the symmetry in the
>alternative definition of variance, particularly because the link between the
>original definition and this version is far from intuitive.
>Lana
>  
>


From dshin@MIT.EDU Wed Dec  7 11:27:27 2005
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In fact, it should be something even more complicated than a quadratic.  
As a hint, you will need to use the approximation ln(1+x)=x to get a 
closed form.

DS

Steven Zhou wrote:

> Hi,
>
> Topic is clear.  I have a question for problem set number 5.  With the 
> answer i got for (a), if i were to solve for K that minimized that 
> expression, I get a quadratic equation.  Is it that complicated, or 
> did I do part (a) wrong. Thanks.
>
> - Steven Zhou
>

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Hmm...not sure what the question is here.

Maybe it would help if you looked at it like this:
Var[R] = E[(R-c)^2],  for a constant c=E[R].

Now we are talking about essentially the expectation of the square of a
random variable.  Does that make sense?

-Hanson



On Wed, 7 Dec 2005, Karena Tyan wrote:

> I was a little confused over the definition of variance on page 13.
>
> Var[R]::= E[(R - E[R])^2] makes it seem as if it's the expected value of
> the squares of (R - E[R]), so I thought this meant calculating the sums
> of the squares and dividing by the probability of each happening.  It
> seems to be more like the square of the expected (R - E[R]) and then the
> expected value of that...or something.  In other words, I'm confused
> over the definition of variance.
>
> - Karena
>
> -- 410 Memorial Drive Cambridge, MA 02139 (585)957-5923
>

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Harrison King Hall wrote:

> David-
> Even after our talk today I still had trouble with variance and using 
> it to apply Chebyshev's theorem.  For that matter is is taking me a 
> long time to figure out Markov's as well.  I think that I will get it 
> by the time we go over it in class.  I think that my problem lies in 
> the fact that I don't really get E[R-c] where R is a random variable 
> and c is some constant.  I think this can be explained by linearity of 
> expectation and E[c]=c, but I am not sure.

Yes, thats exactly right - that's why E[R-c] = E[R] - E[c] = E[R] - c.

> Well, that is all I have to say, but I need to tell you that there is 
> a high probability that I will not be in class on Friday since I have 
> an away swim meet.  I will get back to you on the exact time that I 
> will be leaving and whether it means I will miss class.

No prob.

> -Harrison
>

From mitras@theory.csail.mit.edu Wed Dec  7 11:29:53 2005
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From: Sayan Mitra <mitras@theory.csail.mit.edu>
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Subject: Re: [Sayan] Week 14 Comments
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Hi Praveen,
Var[X_i] is calculated usinf theorem 8.1 which says that
Var[X_i] = E[X_i^2] - (E[X_i])^2.
E[X_i] = 1 x1/2 + 0 = 1/2
(E[X_i])^2 = 1/4
E[X_i^2] = 1^2 x 1/2 + 0 = 1/2
So, Var[X_i] = 1/2 - 1/4 =1/4.

-sayan



Praveen Pamidimukkala wrote:

> Hi,
>
> My question pertains to the TP as opposed to the reading.  In TP.14.3, 
> it reads:
>
>
> "Suppose you flip a fair coin 100 times. The coin flips are all 
> mutually independent.
>
> 3. What is the variance of the number of heads?
>
> Let Xi be the random variable whose value is 1 if the ith coin flip is 
> heads. Then
> Var[Xi] = 1/2 - (1/2)2 = 1/4. So Var[X1 + ... + X100] = 100·(1/4) = 25.
> (Note we can use additivity of variance here since our flips are 
> independent.) ] "
>
> I don't understand the calculation for Var[Xi].
>
> Thanks,
> Praveen
>


From sil_03@MIT.EDU Wed Dec  7 11:35:17 2005
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From: "Silvia F. Baptista" <sil_03@MIT.EDU>
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I'm confused about the Markov and Chebyshev upper bounds.  Especially when
you're given a lower bound.

Silvia

From aston@MIT.EDU Wed Dec  7 11:38:21 2005
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From: "Aston Motes" <aston@MIT.EDU>
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Subject: [Hanson] Week 14 Comments
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I had an oddly intuitive sense of Markov's theorem as far as the online
questions went, though I realized once I saw the formula for it that I
didn't really understand why what I was doing worked. The explanation is
pretty nice (on pages 10 and 11). 

 

I was wondering, though, if there's some way to tell how well the Markov
bound actually bounds examples. Is it due to the variance?

 

            - Aston


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<p class=3DMsoNormal><font size=3D2 face=3DArial><span =
style=3D'font-size:10.0pt;
font-family:Arial'>I had an oddly intuitive sense of Markov&#8217;s =
theorem as
far as the online questions went, though I realized once I saw the =
formula for
it that I didn&#8217;t really understand why what I was doing worked. =
The
explanation is pretty nice (on pages 10 and 11). =
<o:p></o:p></span></font></p>

<p class=3DMsoNormal><font size=3D2 face=3DArial><span =
style=3D'font-size:10.0pt;
font-family:Arial'><o:p>&nbsp;</o:p></span></font></p>

<p class=3DMsoNormal><font size=3D2 face=3DArial><span =
style=3D'font-size:10.0pt;
font-family:Arial'>I was wondering, though, if there&#8217;s some way to =
tell
how well the Markov bound actually bounds examples. Is it due to the =
variance?<o:p></o:p></span></font></p>

<p class=3DMsoNormal><font size=3D2 face=3DArial><span =
style=3D'font-size:10.0pt;
font-family:Arial'><o:p>&nbsp;</o:p></span></font></p>

<p class=3DMsoNormal><font size=3D2 face=3DArial><span =
style=3D'font-size:10.0pt;
font-family:Arial'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;=
&nbsp;&nbsp; - Aston<o:p></o:p></span></font></p>

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From ozcan@MIT.EDU Wed Dec  7 12:09:04 2005
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Section 3, Conditional Expectation, Theorem 3.4

This is an amazing way of finding the expectation for pair-wise disjoint 
events. 


From jstritar@MIT.EDU Wed Dec  7 12:25:02 2005
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Subject: [Sayan] Reading comments
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Sorry for being a bit late... I actually did the reading/TP on Monday
and forgot to send the email.

3. Conditional Expectation

I like the law of total expectation. It seems like it usually comes in
very handy. I feel like you might be able to make some cool AI using
some sort of conditional expectations + bayesian probabilities.

Jon


From meyer@imap.theory.csail.mit.edu  Wed Dec 07 10:56:59 2005
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I don't really understand the standard deviation stuff.  Lecture should 
help, I think.

Rachel

From nedzel@MIT.EDU Wed Dec  7 13:35:18 2005
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What is the significance of Markov and Chebyshev error bounds as they
relate to statistical sampling theory?

- David

From dangut@MIT.EDU Wed Dec  7 13:44:43 2005
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I'm still a little confused, is variance related to independence? Does
independence necessarily imply normal probability of variance?

From pjs@MIT.EDU Wed Dec  7 15:37:24 2005
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From: PJ Steiner <pjs@MIT.EDU>
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I found the distinction between pairwise independence and independence 
interesting.

pjs

From meyer@imap.theory.csail.mit.edu  Wed Dec 07 10:56:56 2005
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Subject: [Jelani] This Week's Comment
Date: Wed, 7 Dec 2005 10:56:41 -0500
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I don't really understand the standard deviation stuff.  Lecture should 
help, I think.

Rachel


