Conditional moment generating functions

In summary, the given problem involves a continuous uniform random variable U in the time interval (0,2) and a conditional distribution T|U modeled by the mgf \frac{1}{1-ut}. To find the mean and variance of T|U, we use the method of finding the mean and variance from the mgf. To find the unconditional mean and variance of T, we use the concept of double expectation.
  • #1
muso07
54
0

Homework Statement


Random variable U is continuous uniform in the time interval (0,2)
T|U (T given U) is modeled by the mgf [tex]\frac{1}{1-ut}[/tex]
Find:
a) E(U)
b) E(T|U) and Var(T|U)
c) E(T) and Var(T)


Homework Equations





The Attempt at a Solution


a) This one was fine, E(U)=1
b) I know E(X)=m'(0), but how does it work with conditional distributions?
c) Again, not sure how I find the marginal distribution of T from the conditional mgf.

Any pointers appreciated. :)
 
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  • #2
How do you use any mgf to find the mean of the underlying variable? The same method applied to

[tex]
\frac 1 {1-ut}
[/tex]

will give E(T | U) (it will be a function of U), and you can also use the conditional mgf to find V(T | U) (another function of U). To find the unconditional expectation and Variance of T, use the notion of double expectation. (E(T) = E(E(T|U)))
 

Related to Conditional moment generating functions

1. What is a conditional moment generating function (CMGF)?

A conditional moment generating function is a mathematical function used to describe the properties of a random variable given certain conditions or constraints. It is a conditional version of the moment generating function, which describes the properties of a random variable without any conditions.

2. How is a CMGF different from a regular moment generating function?

A CMGF takes into account additional information or constraints on a random variable, while a regular moment generating function describes the properties of a random variable without any conditions. In other words, a CMGF allows us to describe the properties of a random variable when certain conditions or constraints are present.

3. What is the purpose of a CMGF?

The purpose of a CMGF is to provide a way to describe the properties of a random variable given certain conditions or constraints. It is particularly useful in statistical analysis and hypothesis testing, where we may need to take into account additional information or constraints in order to make accurate conclusions.

4. How is a CMGF used in statistical analysis?

CMGFs are used in statistical analysis to calculate the moments of a random variable and to derive important properties such as mean, variance, and higher moments. These properties are then used to make inferences and test hypotheses about the underlying data.

5. Can a CMGF be used to describe the properties of multiple random variables?

Yes, a CMGF can be used to describe the properties of multiple random variables, either jointly or individually. This allows us to take into account the relationships and dependencies between multiple variables when making statistical inferences or testing hypotheses.

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