List questions from conditional-expectation

Published on 03 Apr 2026 - 1:16

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Textbook error? Or my own error?

Published on 05 Feb 2015 - 4:54

492

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The function given by the Rao-Blackwell theorem is a statistic

Published on 04 Apr 2026 - 0:57

83

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formal proof $\mathbb E[g_2(Y) \cdot g_1(X)|Y]=g_2(Y) \cdot \mathbb E[g_1(X)|Y]$

Published on 03 Apr 2026 - 2:58

66

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show conditional expectations are equal in distribution

Published on 06 Feb 2015 - 5:39

45

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showing conditional independence

Published on 06 Feb 2015 - 21:57

302

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About infinite conditional variance and infinite unconditional variance

Published on 08 Feb 2015 - 4:24

253

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A quick question on Conditional Expectation

Published on 08 Feb 2015 - 11:10

1.1k

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Are there any examples of a random variable with infinite second moment and finite variance?

Published on 08 Feb 2015 - 19:07

77

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Expectations using Indicator Functions

Published on 10 Feb 2015 - 14:09

22

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Conditional expectations one more time

Published on 31 Mar 2026 - 7:12

365

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Conditional expectation of discrete random variable given noisy observation

Published on 03 Apr 2026 - 2:55

1.1k

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Conditional expectation acting on square of a random variable

Published on 11 Feb 2015 - 1:56

87

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If $X=E[X\mid Y]$ what can we say about $X$ and $Y$.

Published on 03 Apr 2026 - 4:43

161

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Suppose $X=E[X\mid X+Z]$ where $X$ and $Z$ are independent

Published on 03 Apr 2026 - 4:42

List Question

Show that $E[(Y-E[Y|X])*(E[Y|X]-g(X))]=0$

Textbook error? Or my own error?

The function given by the Rao-Blackwell theorem is a statistic

formal proof $\mathbb E[g_2(Y) \cdot g_1(X)|Y]=g_2(Y) \cdot \mathbb E[g_1(X)|Y]$

show conditional expectations are equal in distribution

showing conditional independence

About infinite conditional variance and infinite unconditional variance

A quick question on Conditional Expectation

Are there any examples of a random variable with infinite second moment and finite variance?

Expectations using Indicator Functions

Conditional expectations one more time

Conditional expectation of discrete random variable given noisy observation

Conditional expectation acting on square of a random variable

If $X=E[X\mid Y]$ what can we say about $X$ and $Y$.

Suppose $X=E[X\mid X+Z]$ where $X$ and $Z$ are independent

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