Joint probability density function problem

In summary, the conversation discusses the joint probability density function of three continuous random variables and how to prove a specific probability using permutations and subsets. The subsets defined by different permutations may have some overlap, and the discussion also touches on the invariance of a function under swapping of parameters and the information contained in its restriction to the defined subsets.
  • #1
braindead101
162
0
Suppose that h is the probability density function of a continuous random variable.
Let the joint probability density function of X, Y, and Z be
f(x,y,z) = h(x)h(y)h(z) , x,y,zER

Prove that P(X<Y<Z)=1/6


I don't know how to do this at all. This is suppose to be review since this is a continuation course, but I didn't take the previous course

Any help would be greatly appreciated
 
Physics news on Phys.org
  • #2
For each permutation [itex]\sigma\in S_n[/itex] you can define a following subset of [itex]\mathbb{R}^n[/itex].

[tex]
X_{\sigma} := \{x\in\mathbb{R}^n\;|\; x_{\sigma(1)} \leq x_{\sigma(2)} \leq \cdots \leq x_{\sigma(n)}\}
[/tex]

Some relevant questions: Are these subsets (with different [itex]\sigma[/itex]) mostly/essentially disjoint? In what sense they are the same shape? How many of these subsets are there? What is the union [tex]\bigcup_{\sigma\in S_n} X_{\sigma}[/tex]? If a function [itex]f:\mathbb{R}^n\to\mathbb{R}[/itex] is invariant under a swapping of parameters like this

[tex]
f(x_1,\ldots, x_n) = f(x_1,\ldots, x_{i-1},x_j, x_{i+1},\ldots x_{j-1}, x_i, x_{j+1},\ldots, x_n),
[/tex]

what information does the restriction of [tex]f|_{X_{\sigma}}[/tex] contain?

This is all related to your problem.
 
Last edited:

FAQ: Joint probability density function problem

What is a joint probability density function (PDF)?

A joint probability density function (PDF) is a mathematical function that describes the probability distribution of two or more random variables. It is used to determine the likelihood of different outcomes when two or more variables are involved in a system.

How is a joint PDF different from a regular PDF?

A regular PDF describes the probability distribution of a single random variable, whereas a joint PDF describes the probability distribution of two or more random variables. This means that a joint PDF takes into account the relationship between multiple variables, rather than just one variable.

How is a joint PDF calculated?

A joint PDF is calculated by taking the partial derivatives of the joint cumulative distribution function (CDF). The CDF represents the probability that the variables are less than or equal to a specific value, while the partial derivatives are used to find the probability density at a specific point. This process is also known as differentiation.

What is the purpose of using a joint PDF?

The purpose of using a joint PDF is to understand the relationship and interactions between two or more variables in a system. It allows us to determine the likelihood of different outcomes and make predictions based on the given variables and their probability distributions.

When is a joint PDF used in scientific research?

A joint PDF is commonly used in scientific research when analyzing complex systems with multiple variables. It is particularly useful in fields such as physics, economics, and engineering, where the behavior of a system is influenced by multiple factors and understanding the relationship between these factors is crucial for making accurate predictions.

Back
Top