A question about probability measure theory

In summary, probability measure theory is a branch of mathematics that provides a formal framework for understanding and analyzing probabilities, random variables, and events. It differs from regular probability by being more rigorous and formal. This theory has practical applications in various fields such as finance, physics, statistics, and computer science. Key concepts include probability spaces, random variables, events, measures, and sigma-algebras. While it can be difficult to understand due to its abstract nature, it can be learned with proper study and practice.
  • #1
hwangii
5
0
Hi all,
I have a question about measure theory:
Suppose we have probability space [tex](\mathbb{R}^d,\mathcal{B}^d,\mu)[/tex] where [tex]\mathcal{B}^d[/tex] is Borel sigma algebra.
Suppose we have a function
[tex]u:\mathbb{R}^d\times \Theta\rightarrow \mathbb{R}[/tex] where [tex] \Theta\subset\mathbb{R}^l,l<\infty[/tex] and [tex]u[/tex] is continuous on [tex]\mathbb{R}^d\times \Theta[/tex].
Now consider the function [tex]G:\Theta\rightarrow[0,1][/tex] defined as follows:
[tex]G(\theta)=\int\limits_{\epsilon\in\mathbb{R}^d}\mathbb{I}\{u(\epsilon, \theta)\geq 0\} \mu (d\epsilon)[/tex]
where [tex]\mathbb{I}\{P\}[/tex] is an indicator function equal to 1 if P is true and 0 otherwise.
Is [tex]G(\theta)[/tex] continuous on [tex]\Theta[/tex]?

If you know the answer, could you please also tell me what kind of math books I need to look to find more about this? I would like to more about this by reading such text.

Thanks a lot!
 
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  • #2


Hi there,

Thank you for your question. To answer your first question, yes, G(\theta) is continuous on \Theta. This can be proven using the continuity of u on \mathbb{R}^d\times \Theta and the continuity of \mu on \mathcal{B}^d. Specifically, for any \theta_0\in\Theta and any \epsilon>0, we can find a \delta>0 such that for all \theta\in\Theta, if |\theta-\theta_0|<\delta, then |u(\epsilon,\theta)-u(\epsilon,\theta_0)|<\epsilon. This means that for any \theta_0, if we take a small enough neighborhood around \theta_0, the values of u(\epsilon,\theta) will be close to u(\epsilon,\theta_0) for all \epsilon\in\mathbb{R}^d. And since \mu is continuous, the integral will also be close to the value at \theta_0. Therefore, G(\theta) is continuous on \Theta.

To learn more about this topic, I recommend looking into measure theory textbooks such as "Probability and Measure" by Patrick Billingsley or "Real Analysis" by Royden and Fitzpatrick. These texts cover the basics of measure theory and can provide a deeper understanding of concepts such as probability spaces, Borel sigma algebras, and integrals. Additionally, you may also find resources on mathematical analysis and functional analysis helpful in understanding the continuity of functions. I hope this helps and good luck with your studies!
 

1. What is probability measure theory?

Probability measure theory is a branch of mathematics that deals with the mathematical foundations of probability. It is used to formalize and analyze the concepts of probability, random variables, and events.

2. How is probability measure theory different from regular probability?

Regular probability deals with calculating the likelihood of an event occurring based on its sample space. Probability measure theory, on the other hand, provides a more rigorous and formal framework for understanding and manipulating probabilities.

3. What are some real-world applications of probability measure theory?

Probability measure theory has practical applications in fields such as finance, physics, statistics, and computer science. It is used to model and analyze complex systems, make predictions, and assess risk.

4. What are some key concepts in probability measure theory?

Some key concepts in probability measure theory include probability spaces, random variables, events, measures, and sigma-algebras. These concepts are used to define and formalize the underlying principles of probability.

5. Is probability measure theory difficult to understand?

Probability measure theory can be challenging to understand, as it involves abstract mathematical concepts and notation. However, with proper study and practice, it can be grasped by anyone with a solid foundation in mathematics.

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