Probability help/sigma-algebras

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In summary, to prove that F is a sigma-algebra on A, we need to show that F satisfies the properties of a sigma-algebra, namely that it contains A, is closed under complementation, and is closed under countable unions. This can be done by showing that A is in F, the complement of A intersect E is in F, and the countable union of A intersect E_i is in F, using the definition of a sigma-algebra and the properties of sets.
  • #1
FTaylor244
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Let (S, ε, P) be a probability space and let A be an element of ε with P(A)>0. Let F={AπE :E is an element of ε }
-Prove that F is a sigma-algebra on A.



Not sure even where to go with this really. I know that to be a sigma-algebra has to be closed under complementation and countable unions. I'm not very good with proofs, and just a push in the right direction would help me out a ton. Thanks!
 
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  • #2
So you will need to show that:

- A is in F
- The complement of A cap E is in F
- A countable union U_i A cap E_i is in F.

All you need to know are the properties of sets and that epsilon is a sigma algebra. Try it and show what you have tried.
 
  • #3
Oh, I was under the impression I had to do it the other way around, such that F is in A, etc..maybe that's why I'm having such a hard time here.

Ok so...

A is in F because F=A intersect E, and since an intersect of A is in F, then all of A has to be in F (not sure how to write this properly)
F=AnE is the same as F=(A^c U E^c)^c (^c=complement)
not really sure how to go about the last one
 
  • #4
FTaylor244 said:
A is in F because F=A intersect E, and since an intersect of A is in F, then all of A has to be in F (not sure how to write this properly)
F=AnE is the same as F=(A^c U E^c)^c (^c=complement)
not really sure how to go about the last one

F is a set of subsets of A, namely the set of A cap E for E in the sigma algebra. Check the definition of a sigma algebra. Try again keeping that in mind.
 
  • #5


First, we need to show that F is closed under complementation. This means that for any AπE in F, the complement AπE^c must also be in F.

Since A is an element of ε, we know that A^c is also an element of ε. Therefore, AπE^c is a subset of A, and since AπE is also a subset of A, we have AπE^c = Aπ(E^c ∩ A). Since ε is a sigma-algebra, E^c ∩ A is also an element of ε. Therefore, AπE^c is in F, and F is closed under complementation.

Next, we need to show that F is closed under countable unions. This means that for any sequence of sets {AπE1, AπE2, ...} in F, their union must also be in F.

Let B = ⋃i=1∞ AπEi. Since A is an element of ε, we know that AπEi is also an element of ε for all i. Therefore, B is a union of sets in ε, and since ε is a sigma-algebra, B is also an element of ε. This means that B is an element of F, and F is closed under countable unions.

Finally, we need to show that A is an element of F. Since A is an element of ε, we can write A as AπS, where S is the entire sample space. Since S is an element of ε, we have AπS is in F. Therefore, A is an element of F.

Overall, we have shown that F is closed under complementation, countable unions, and contains A. Therefore, F is a sigma-algebra on A.
 

1. What is probability and why is it important in science?

Probability is a measure of the likelihood of an event or outcome occurring. In science, it is important because it allows us to make predictions and draw conclusions based on data. It helps us understand the uncertainty inherent in many scientific phenomena and make informed decisions.

2. What is a sigma-algebra and how does it relate to probability?

A sigma-algebra is a collection of subsets of a sample space that satisfies certain properties. In probability theory, it is used to define the events for which probabilities can be assigned. It ensures that all possible outcomes are included and that the rules of probability are satisfied.

3. How do you calculate probabilities using sigma-algebras?

To calculate probabilities using sigma-algebras, you need to first define the sample space and the events of interest. Then, you can use mathematical tools such as set theory and measure theory to determine the probabilities of these events. The sigma-algebra provides a framework for these calculations.

4. What is the difference between discrete and continuous probability distributions?

A discrete probability distribution is one in which the possible outcomes are countable, such as rolling a die or flipping a coin. A continuous probability distribution is one in which the possible outcomes are uncountable, such as measuring the height of a person. The calculations and formulas used for these two types of distributions are different.

5. How is probability used in statistical inference?

Probability is used in statistical inference to make conclusions about a population based on a sample of data. It allows us to quantify the uncertainty in our estimations and determine the likelihood of certain outcomes. Statistical techniques such as hypothesis testing and confidence intervals rely on probability to make inferences about a population.

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