Construction of a probability space.

In summary, the conversation discussed the construction of a probability space (Ω, A, P[·]) where A is a set containing Ω and the empty set, and P[ω] = 1 for Ω and P[∅] = 0. The conversation also mentioned using discrete sets, such as a biased coin, to demonstrate the properties of this probability space.
  • #1
chocolatefrog
12
0
Q. Exhibit (if such exists) a probability space, denoted by (Ω, A, P[·]), which satisfies the following. For A1 and A2 members of A, if P[A1] = P[A2], then A1 = A2.

Answer: A = {Ω, ∅}, P[Ω] = 1 and P[∅] = 0. Is this a valid answer to the above question?
 
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  • #3
haruspex said:
Looks ok to me.

Thank you, haruspex!
 
  • #4
If you want something more interesting, you can work with discrete sets. Example: 2 elements (A,B) with P(A) = 1/3 and P(B) = 2/3.
 
  • #5
mathman said:
If you want something more interesting, you can work with discrete sets. Example: 2 elements (A,B) with P(A) = 1/3 and P(B) = 2/3.

Thanks, mathman. I ended up doing something similar; constructed such a space for a biased coin.
 

1. What is a probability space?

A probability space is a mathematical framework used to model and analyze random events or processes. It consists of a sample space, a set of all possible outcomes, and a probability measure that assigns a numerical value to each outcome.

2. How is a probability space constructed?

A probability space is constructed by defining a sample space and a probability measure. The sample space can be discrete, such as a set of numbers, or continuous, such as an interval on the number line. The probability measure assigns a probability to each element in the sample space, ensuring that the sum of all probabilities is equal to 1.

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

A probability space is a mathematical framework, while a probability distribution is a function that describes the probabilities of possible outcomes. A probability distribution can be constructed using a probability space, but they are not the same thing.

4. Why is it important to construct a probability space?

Constructing a probability space allows us to mathematically model and analyze random events or processes. It provides a framework for understanding the likelihood of different outcomes and can help us make informed decisions or predictions based on probability.

5. What are some real-world applications of constructing a probability space?

Probability spaces are used in a wide range of fields, including physics, finance, and statistics. Some examples of real-world applications include predicting stock market fluctuations, analyzing the risk of natural disasters, and understanding the spread of diseases.

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