Finding Conditional Probability Distribution for Discrete Random Variables

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In summary, a joint distribution problem is a statistical problem that involves determining the probability distribution of multiple related random variables. It differs from a marginal distribution problem, which focuses on a single variable. Joint distribution problems have applications in finance, economics, and engineering and can be solved using various statistical techniques. However, they are limited by the assumptions and complexities involved in modeling multiple variables together.
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brad sue
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Hi,
I would like some help with this problem

If X1 and X2 are two discrete random variables with joint probability distribution given by the following bivariate table.(table attached)

1-Find the conditional probability distribution of X2 given X1.
thank you
 

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  • #2
Dontcha want to take a crack at it and show us what you've got?

(By the way, if memory serves you aren't going to have just one conditional probability distribution. Rather, you're going to have one for each value of [itex]x_1[/itex]).
 

Related to Finding Conditional Probability Distribution for Discrete Random Variables

1. What is a joint distribution problem?

A joint distribution problem is a statistical problem that involves determining the probability distribution of two or more random variables that are related to each other. It is used to model the simultaneous behavior of multiple variables and can be used to make predictions and analyze data.

2. How is a joint distribution problem different from a marginal distribution problem?

A marginal distribution problem involves determining the probability distribution of a single random variable, while a joint distribution problem involves determining the probability distribution of multiple random variables together. In other words, a joint distribution problem considers the relationship between variables, while a marginal distribution problem looks at each variable individually.

3. What are some real-world applications of joint distribution problems?

Joint distribution problems are commonly used in fields such as finance, economics, and engineering to model and analyze complex systems. For example, joint distribution problems can be used to model the relationship between interest rates and stock prices in finance, or the relationship between supply and demand in economics.

4. How do you solve a joint distribution problem?

Solving a joint distribution problem involves using statistical techniques such as probability theory, calculus, and linear algebra. The specific method used will depend on the complexity of the problem and the type of data being analyzed. Some common techniques include using joint probability mass functions, joint probability density functions, and conditional probability distributions.

5. What are the limitations of joint distribution problems?

Joint distribution problems are limited by the assumptions and simplifications made in the modeling process. In real-world scenarios, there may be many variables and factors that cannot be accurately captured in a joint distribution model. Additionally, joint distribution problems may become computationally complex and difficult to solve as the number of variables increases.

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