Combining Probability Measures: A Concrete Illustration and Proof"

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In summary, the conversation discussed the definition of a probability measure and the conditions it must satisfy. It also explored the idea of a mixture of probability measures and how it can also be a probability measure under certain conditions. A concrete illustration was given, and the process for showing the axioms for the new measure was explained. The conversation ended with a successful response to the question.
  • #1
Alexsandro
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Do Someone know reply this question? It seems easy, but I am not being able to resolve it.

Show that if P and Q are two probability measures defined on the same (countable) sample space, then a.P + b.Q is also a probability measure for any two nonnegative numbers a and b satisfying a + b = 1. Give a concrete illustration of such a mixture
 
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The basic approach is to look at the definition of what a probability measure is. Then you go through and check if the mixture as defined in the question satisfies those conditions given that P and Q are already probability measures.
 
  • #3
Probability measure

David said:
The basic approach is to look at the definition of what a probability measure is. Then you go through and check if the mixture as defined in the question satisfies those conditions given that P and Q are already probability measures.


David, I know that an probability measure on same space [itex]\Omega[/itex] is a function of subsets of [itex]\Omega[/itex] satisfying three axioms:

(i) For every set [itex]A \subset \Omega[/itex], the value of the function is a non-negative number: P(A) [itex]\geqslant[/itex] 0.

(ii) For any two disjoint sets A and B, the value of the function for their union A + B is equal to the sum of its value for A and its value for B:

P(A + B) = P(A) + P(B) provided A.B = [itex]{\O}[/itex].

(iii) The value of the function for [itex]\Omega[/itex] (as a subset) is equal to 1:

P([itex]\Omega[/itex]) = 1.

but I don't know how to show that a.P + b.Q is too.
 
  • #4
Perhaps you're not sure of the process foor how to show the axioms apply to the new measure so I'll give you an example with the first one.

Remember that the a.P + b.Q is a function that maps a subset A onto a real number. You already know some properties of how the functions P and Q work and you use them to work out the properties of the combined function.

So for the first axiom, we want to show that a.P + b.Q (which I'll call R for the moment) is non-negative. i.e. that R(A) >=0 for any A. The definition of P and Q say that P(A)>=0 and Q(A)>=0. Also, from the conditions set in the question, a and b are >=0. So for any A, the function R(A) = a.P(A) + b.Q(A) is a non-negative times a non-negative plus a non-negative times a non-negative. This total is non-negative, which is what you need for the axiom to hold.

What I just did there might seem overkill, but explicitly remembering that you can do this for a particular subset A and then realizing that it works for any subset A will sometimes get you to the answer you need. That will be helpful to organize yout thoughts for axiom ii. Axiom iii works just like the proof of axiom i.
 
  • #5
response

David said:
Perhaps you're not sure of the process foor how to show the axioms apply to the new measure so I'll give you an example with the first one.

Remember that the a.P + b.Q is a function that maps a subset A onto a real number. You already know some properties of how the functions P and Q work and you use them to work out the properties of the combined function.

So for the first axiom, we want to show that a.P + b.Q (which I'll call R for the moment) is non-negative. i.e. that R(A) >=0 for any A. The definition of P and Q say that P(A)>=0 and Q(A)>=0. Also, from the conditions set in the question, a and b are >=0. So for any A, the function R(A) = a.P(A) + b.Q(A) is a non-negative times a non-negative plus a non-negative times a non-negative. This total is non-negative, which is what you need for the axiom to hold.

What I just did there might seem overkill, but explicitly remembering that you can do this for a particular subset A and then realizing that it works for any subset A will sometimes get you to the answer you need. That will be helpful to organize yout thoughts for axiom ii. Axiom iii works just like the proof of axiom i.

Thank you for help. I responsed the question with success.
 

1. What is a probability measure?

A probability measure is a mathematical function that assigns a numerical value to each event in a sample space, representing the likelihood of that event occurring. In other words, it quantifies the uncertainty associated with a particular outcome.

2. How is a probability measure different from a probability distribution?

A probability measure is a function that assigns probabilities to events, while a probability distribution is a function that describes the probabilities of all possible outcomes of a random variable. In simpler terms, a probability measure is used for discrete events, while a probability distribution is used for continuous variables.

3. What are the properties of a probability measure?

A probability measure must have certain properties in order to be considered valid. These properties include assigning probabilities between 0 and 1 to all events, assigning a probability of 1 to the entire sample space, and satisfying the additivity property, which states that the probability of the union of two disjoint events is equal to the sum of their individual probabilities.

4. How is probability measure used in real-world applications?

Probability measures are used in a variety of fields, including statistics, finance, and engineering, to make predictions and inform decision-making. For example, probability measures are used in risk assessment to determine the likelihood of certain events, such as natural disasters or stock market fluctuations, and to inform strategies for mitigating or managing these risks.

5. What are some common types of probability measures?

The most commonly used probability measures include the discrete uniform distribution, the binomial distribution, and the normal distribution. These measures are used to model different types of events and can be applied in various contexts, such as in genetics, economics, and psychology.

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