Properties of probability measures

In summary, the conversation discusses the problem of proving that a probability measure is finitely additive, given its countable additivity property. Suggestions are made to consider sets or numbers that act as identities under union or addition, and the importance of proving that P(\emptyset)=0 is mentioned. The conversation ends with the realization that if P(\emptyset) \neq 0, a contradiction would occur.
  • #1
Mathechyst
29
0
I hate it when a fact is so obvious that it isn't obvious how to prove it. Like showing that a subset of a finite set is finite. So ... here goes:

A probability measure [itex]P[/itex] on a [itex]\sigma[/itex]-field [itex]\mathcal{F}[/itex] of subsets of a set [itex]\Omega[/itex] is a function from [itex]\mathcal{F}[/itex] to the unit interval [itex][0,1][/itex] such that [itex]P(\Omega)=1[/itex] and

[tex]
P\left(\bigcup_{m=1}^{\infty}A_m\right)=\sum_{m=1}^{\infty}P\left(A_m\right)
[/tex]

for each pairwise disjoint sequence [itex](A_m:m=1,2,3,\ldots)[/itex] of members of [itex]\mathcal{F}[/itex]. Because [itex]P[/itex] satisfies this summation condition it is said to be countably additive.

The problem is to show that [itex]P[/itex] is finitely additive, that is:

[tex]
P\left(\bigcup_{m=1}^{n}A_m\right)=\sum_{m=1}^{n}P\left(A_m\right)
[/tex]

for each pairwise disjoint finite sequence [itex](A_1,\ldots,A_n)[/itex] of members of [itex]\mathcal{F}[/itex].

Anyone have any hints to toss my way? Thanks.

Doug
 
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  • #2
Well, you might look at the LHS, and try to consider sets that act as an identity under union, so you can set an infinite union equal to a finite union.

Or, you might look at the RHS and consider numbers that act as an identity under addition, so that you can set an infinite sum equal to a finite sum.
 
  • #3
The proof would be trivial if [itex]P(\emptyset)=0[/itex] but that too is a fact that must be proved.

Doug
 
  • #4
Hrm, do you know any disjoint sequences of sets whose union is the empty set?
 
  • #5
I would say there's only one: [itex](\emptyset,\emptyset,\ldots)[/itex].

Doug
 
  • #6
So what happens if [itex]P(\emptyset) \neq 0[/itex]?
 
  • #7
Aha. A contradiction. :smile:
 

1. What are the different types of probability measures?

There are three main types of probability measures: discrete, continuous, and mixed. Discrete probability measures assign probabilities to individual events, while continuous probability measures assign probabilities to intervals of values. Mixed probability measures combine elements of both discrete and continuous measures.

2. How are probability measures used in statistical analysis?

Probability measures are used in statistical analysis to quantify the likelihood of events occurring and to make predictions based on data. They are also used to estimate the parameters of a population based on a sample of data.

3. What is the difference between a probability measure and a probability distribution?

A probability measure is a mathematical function that assigns probabilities to events, while a probability distribution is a mathematical representation of the probabilities of all possible outcomes of a random variable. Essentially, a probability distribution is a visual representation of a probability measure.

4. Can probability measures be negative?

No, probability measures cannot be negative. They must always be non-negative values between 0 and 1, where 0 represents impossibility and 1 represents certainty.

5. How do you calculate the expected value of a probability measure?

The expected value of a probability measure is calculated by multiplying each possible outcome by its corresponding probability and then summing up all of these values. It represents the average value that would be obtained in a large number of repeated trials.

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