Understanding a $sigma$-field
Problem
Let's consider $n$ Bernoulli trials with the probability of getting a success equal to $p$. My task is to find the expected value of getting a success in the first trial on condition that we know how many successes have occurred in all trials.
My attempt
The problem is quite easy. I define two variables
$S_n$ - the amount of successes,
$X$ - the amount of successes in the first trial.
I would like to find the value of $mathbb{E}(X | sigma(S_n))$.
Now I would like to do something with $sigma(S_n)$. I defined new sequence of events:
$$A_k = {S_n = k} tag{1}.$$
Now $sigma(S_n) = sigma(A_0, ldots, A_n)$.
The further calculations are very easy. The answer to this problem is $frac{S_n}{n}$.
What I don't understand
I don't really understand how does $sigma(A_0, ldots, A_n)$ look like. To my mind there are a lot of events that are completely impossible. Let me give you an example.
Let's fix $n=1$. That gives us:
$$A_0, A_1$$
and
$$sigma(A_0, A_1) = {emptyset, Omega = A_0 cup A_1, A_0, A_1 }.$$
Am I correct? How can I interpret $A_0 cup A_1$?
probability probability-theory conditional-expectation
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Problem
Let's consider $n$ Bernoulli trials with the probability of getting a success equal to $p$. My task is to find the expected value of getting a success in the first trial on condition that we know how many successes have occurred in all trials.
My attempt
The problem is quite easy. I define two variables
$S_n$ - the amount of successes,
$X$ - the amount of successes in the first trial.
I would like to find the value of $mathbb{E}(X | sigma(S_n))$.
Now I would like to do something with $sigma(S_n)$. I defined new sequence of events:
$$A_k = {S_n = k} tag{1}.$$
Now $sigma(S_n) = sigma(A_0, ldots, A_n)$.
The further calculations are very easy. The answer to this problem is $frac{S_n}{n}$.
What I don't understand
I don't really understand how does $sigma(A_0, ldots, A_n)$ look like. To my mind there are a lot of events that are completely impossible. Let me give you an example.
Let's fix $n=1$. That gives us:
$$A_0, A_1$$
and
$$sigma(A_0, A_1) = {emptyset, Omega = A_0 cup A_1, A_0, A_1 }.$$
Am I correct? How can I interpret $A_0 cup A_1$?
probability probability-theory conditional-expectation
add a comment |
Problem
Let's consider $n$ Bernoulli trials with the probability of getting a success equal to $p$. My task is to find the expected value of getting a success in the first trial on condition that we know how many successes have occurred in all trials.
My attempt
The problem is quite easy. I define two variables
$S_n$ - the amount of successes,
$X$ - the amount of successes in the first trial.
I would like to find the value of $mathbb{E}(X | sigma(S_n))$.
Now I would like to do something with $sigma(S_n)$. I defined new sequence of events:
$$A_k = {S_n = k} tag{1}.$$
Now $sigma(S_n) = sigma(A_0, ldots, A_n)$.
The further calculations are very easy. The answer to this problem is $frac{S_n}{n}$.
What I don't understand
I don't really understand how does $sigma(A_0, ldots, A_n)$ look like. To my mind there are a lot of events that are completely impossible. Let me give you an example.
Let's fix $n=1$. That gives us:
$$A_0, A_1$$
and
$$sigma(A_0, A_1) = {emptyset, Omega = A_0 cup A_1, A_0, A_1 }.$$
Am I correct? How can I interpret $A_0 cup A_1$?
probability probability-theory conditional-expectation
Problem
Let's consider $n$ Bernoulli trials with the probability of getting a success equal to $p$. My task is to find the expected value of getting a success in the first trial on condition that we know how many successes have occurred in all trials.
My attempt
The problem is quite easy. I define two variables
$S_n$ - the amount of successes,
$X$ - the amount of successes in the first trial.
I would like to find the value of $mathbb{E}(X | sigma(S_n))$.
Now I would like to do something with $sigma(S_n)$. I defined new sequence of events:
$$A_k = {S_n = k} tag{1}.$$
Now $sigma(S_n) = sigma(A_0, ldots, A_n)$.
The further calculations are very easy. The answer to this problem is $frac{S_n}{n}$.
What I don't understand
I don't really understand how does $sigma(A_0, ldots, A_n)$ look like. To my mind there are a lot of events that are completely impossible. Let me give you an example.
Let's fix $n=1$. That gives us:
$$A_0, A_1$$
and
$$sigma(A_0, A_1) = {emptyset, Omega = A_0 cup A_1, A_0, A_1 }.$$
Am I correct? How can I interpret $A_0 cup A_1$?
probability probability-theory conditional-expectation
probability probability-theory conditional-expectation
edited Nov 28 at 6:54
Shashi
7,0361528
7,0361528
asked Nov 26 at 17:08
Hendrra
1,079416
1,079416
add a comment |
add a comment |
1 Answer
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If $S_1$ can't take any values other than $0$ and $1$ (even on null sets) then you are correct.
$A_0 cup A_1$ is the event that either $S_1 = 0$ or $S_1 = 1$ occurs (or both, which in this case isn't possible). You can always think of unions as "or" and of intersections as "and".
In general, $sigma(S_n)$ could be interpreted as the smallest $sigma$-algebra that contains all the information about $S_n$.
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
If $S_1$ can't take any values other than $0$ and $1$ (even on null sets) then you are correct.
$A_0 cup A_1$ is the event that either $S_1 = 0$ or $S_1 = 1$ occurs (or both, which in this case isn't possible). You can always think of unions as "or" and of intersections as "and".
In general, $sigma(S_n)$ could be interpreted as the smallest $sigma$-algebra that contains all the information about $S_n$.
add a comment |
If $S_1$ can't take any values other than $0$ and $1$ (even on null sets) then you are correct.
$A_0 cup A_1$ is the event that either $S_1 = 0$ or $S_1 = 1$ occurs (or both, which in this case isn't possible). You can always think of unions as "or" and of intersections as "and".
In general, $sigma(S_n)$ could be interpreted as the smallest $sigma$-algebra that contains all the information about $S_n$.
add a comment |
If $S_1$ can't take any values other than $0$ and $1$ (even on null sets) then you are correct.
$A_0 cup A_1$ is the event that either $S_1 = 0$ or $S_1 = 1$ occurs (or both, which in this case isn't possible). You can always think of unions as "or" and of intersections as "and".
In general, $sigma(S_n)$ could be interpreted as the smallest $sigma$-algebra that contains all the information about $S_n$.
If $S_1$ can't take any values other than $0$ and $1$ (even on null sets) then you are correct.
$A_0 cup A_1$ is the event that either $S_1 = 0$ or $S_1 = 1$ occurs (or both, which in this case isn't possible). You can always think of unions as "or" and of intersections as "and".
In general, $sigma(S_n)$ could be interpreted as the smallest $sigma$-algebra that contains all the information about $S_n$.
edited Nov 27 at 18:25
answered Nov 26 at 20:01
Tki Deneb
26710
26710
add a comment |
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