A Conditional Distribution Problem

In summary, the question asks for the conditional distribution of S_n given that S_k = y, where S_j is a normal random variable with mean 0 and variance j. The conditional density function can be found using the formula f_{S_n|S_k}(x,y) = \frac{f_{S_n,S_k}(x,y)}{f_{S_k}(y)}, but the numerator f_{S_n,S_k}(x,y) is unknown. S_n and S_k are jointly normal, but the correlation between them is unknown.
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Homework Statement
Let [itex]Z_1, \ldots, Z_n[/itex] be independent standard normal random variables, and let

[tex]S_j = \sum_{i=1}^j Z_i[/itex]

What is the conditional distribution of [itex]S_n[/itex] given that [itex]S_k = y[/itex], for k = 1, ..., n?

The attempt at a solution
I know that [itex]S_j[/itex] is a normal random variable with mean 0 and variance j. The conditional density function is given by:

[tex]f_{S_n|S_k}(x,y) = \frac{f_{S_n,S_k}(x,y)}{f_{S_k}(y)}[/itex]

The denominator is easily found. All that's left to find is the numerator and with that I'll be able to find the conditional distribution function. This is where I'm stuck. I can't think of anything clever to determine [itex]f_{S_n,S_k}(x,y)[/itex].
 
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  • #2
I know that S_n and S_k are jointly normal but I don't know the correlation between them. Any help is appreciated.
 

FAQ: A Conditional Distribution Problem

What is a conditional distribution problem?

A conditional distribution problem is a statistical problem that involves finding the probability of an event A occurring given that another event B has occurred. In other words, it is the probability of A happening, taking into account the information that B has already happened.

What is the difference between a conditional probability and a joint probability?

Conditional probability is the probability of an event occurring given that another event has already occurred. Joint probability, on the other hand, is the probability of two or more events occurring together. In a conditional distribution problem, we are interested in finding the conditional probability, while in a joint distribution problem, we are interested in finding the joint probability.

How do you calculate conditional probabilities?

The conditional probability of an event A given an event B can be calculated using the formula P(A|B) = P(A and B) / P(B), where P(A and B) is the joint probability of events A and B, and P(B) is the probability of event B occurring.

What is the role of Bayes' Theorem in conditional distribution problems?

Bayes' Theorem is a mathematical formula that helps in calculating conditional probabilities. It is often used in conditional distribution problems to update the probability of an event occurring based on new information. The formula is P(A|B) = P(B|A) * P(A) / P(B), where P(A) and P(B) are the probabilities of events A and B, and P(B|A) is the conditional probability of event B given that event A has occurred.

What are some real-world applications of conditional distribution problems?

Conditional distribution problems are commonly used in various fields such as finance, healthcare, and marketing. For example, in finance, conditional probabilities can be used to predict the likelihood of a stock market crash given certain economic indicators. In healthcare, conditional probabilities can be used to determine the likelihood of a patient developing a certain disease based on their medical history. In marketing, conditional probabilities can be used to target specific customer segments based on their purchasing behavior.

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