Moment Generating Function w/ Condtional Expectation

In summary, we are given a problem where \theta is distributed as a gamma distribution with parameters \alpha and \lambda, where \alpha is a positive integer. We are also given that X has a Poisson distribution with mean \theta, and we are asked to find the unconditional distribution of X. To do this, we need to find the moment generating function (MGT) of X. The first attempt at finding this MGT is unsuccessful, so we must take a different approach. By using the definition of the MGT and solving for the integral, we can find that the unconditional distribution of X is e\alpha \lambda \frac{\alpha^{\alpha}}{\Gamma(\alpha)} \frac{\Gamma(\alpha)}{\
  • #1
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Homework Statement



Suppose [tex]\theta [/tex] ~ [tex]~ gamma(\alpha , \lambda) [/tex] where alpha is a positive integer. Conditional on [tex] \theta[/tex], X has a Poission distribution with mean [tex] \theta [/tex]. Find the unconditional distribution of X by finding it's MGT.

Homework Equations





The Attempt at a Solution



So, this is what I interpreted the problem as:

X = E[E[X|[tex]\theta[/tex]]] = E[[tex]\theta[/tex]] = [tex]\alpha \lambda[/tex]

ext = e[tex]\alpha \lambda[/tex]t

It's as far as I got. Any hints?

or would I have to do this:

ext = E[E[ext|[tex]\theta[/tex]]]
 
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  • #2
= E[e\alpha \lambdat]= e\alpha \lambda \int_0^{\infty} \frac{\alpha^{\alpha}}{\Gamma(\alpha)} t^{\alpha - 1} e^{-\lambda t} dt = e\alpha \lambda \frac{\alpha^{\alpha}}{\Gamma(\alpha)} \int_0^{\infty} t^{\alpha - 1} e^{-\lambda t} dt = e\alpha \lambda \frac{\alpha^{\alpha}}{\Gamma(\alpha)} \frac{\Gamma(\alpha)}{\lambda^{\alpha}}
 

Related to Moment Generating Function w/ Condtional Expectation

What is a moment generating function?

A moment generating function is a mathematical function that is used to characterize a probability distribution. It is defined as the expected value of the exponential function raised to the power of the random variable.

How is the moment generating function related to conditional expectation?

The moment generating function of a random variable can be used to calculate the conditional expectation of that variable. This is done by taking the first derivative of the moment generating function and evaluating it at the desired value of the conditional expectation.

What is the purpose of using a moment generating function with conditional expectation?

The moment generating function with conditional expectation allows for the calculation of probabilities and expected values in more complex scenarios where multiple variables are involved. It can also help in understanding the relationship between different variables in a probability distribution.

What are some properties of a moment generating function with conditional expectation?

Some properties of a moment generating function with conditional expectation include linearity, which means that the moment generating function of a sum of random variables is equal to the product of their individual moment generating functions. Another property is the probability generating function, which is the moment generating function evaluated at the value of 1. This represents the probability of the random variable being equal to 0.

How is a moment generating function with conditional expectation used in practical applications?

Moment generating functions with conditional expectation are used in various fields such as finance, economics, and statistics. They can help in modeling and analyzing complex systems and can also be used in hypothesis testing and confidence intervals. In finance, they are used to calculate the value of options and other financial derivatives.

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