sauravrt said:mathman, thanks for the reply.
So if there are two normal random variables, the a bivariate normal distribution is always defined between them?
If I have random variables, each with different distribution, even so it is possible to find a joint distribution between them?
Saurav
The "G: Joint Distribution of Random Variables" refers to the probability distribution of two or more random variables occurring together in a system. It describes the relationship between these variables and their likelihood of occurring simultaneously.
The joint distribution of random variables is typically represented using a joint probability mass function (PMF) for discrete variables or a joint probability density function (PDF) for continuous variables. These functions provide a mathematical representation of the probabilities of all possible combinations of the variables.
The joint distribution considers the probabilities of two or more variables occurring together, while the marginal distribution looks at the probability of each individual variable occurring independently. In other words, the joint distribution gives a full picture of the relationship between variables, while the marginal distribution focuses on one variable at a time.
The joint distribution of random variables is useful in data analysis because it allows us to understand the relationship between multiple variables and how they may affect each other. It can also be used to make predictions and identify patterns in data sets with multiple variables.
Understanding the joint distribution of random variables is important because it allows us to make more accurate predictions and draw meaningful insights from data sets. It also helps in identifying any dependencies or correlations between variables, which can be useful in decision making and problem solving in various fields such as economics, finance, and engineering.