Mixed Distributions Homework: Showing Marginal Distribution of X

In summary: Using some algebraic manipulation, we get:\frac{h^{h-1}}{(h-1)!} \frac{(\alpha + k - 1)(\alpha + h - 1)(\alpha + x - 1)}{(\alpha - 1)(h - 1)(k - 1)} \frac{\Gamma
  • #1
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Homework Statement



Let X ~ Poission ([tex]\lambda \theta[/tex]). Suppose [tex]\lambda[/tex] ~ gamma (h,h-1) and [tex]\theta[/tex] ~ generalized Pareto ([tex]\alpha[/tex], h-1,k). Show that the marginal distribution of X is

[tex]\frac{\Gamma(\alpha + k) \Gamma(\alpha + h) \Gamma(\alpha + x) \Gamma(k + x)}{\Gamma(\alpha) \Gamma(h) \Gamma(k) \Gamma(\alpha + h + k + x)x!}[/tex]

Homework Equations





The Attempt at a Solution



My prof didnt really explain it.

From what I've gathered in the book it's:

[tex]\int^{\infty}_{- \infty} d \lambda d \theta[/tex] of the distribution of X, lambda, and theta.
 
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  • #2


Firstly, I would like to clarify that X is not a distribution, but rather a random variable. The distribution in question is the Poisson distribution, denoted by X ~ Poisson(\lambda \theta).

To find the marginal distribution of X, we need to integrate out the variables \lambda and \theta from the joint distribution of X, \lambda, and \theta.

Using the definition of the Poisson distribution, we have:

P(X = x) = \frac{(\lambda \theta)^x e^{-\lambda \theta}}{x!}

To integrate out \lambda, we use the fact that \lambda ~ gamma(h,h-1). We know that the gamma distribution has the following probability density function:

f(\lambda) = \frac{h^{h-1}}{\Gamma(h)} \lambda^{h-1} e^{-h \lambda}

Therefore, the integral becomes:

\int^{\infty}_{0} \frac{h^{h-1}}{\Gamma(h)} \lambda^{h-1} e^{-h \lambda} \frac{(\lambda \theta)^x e^{-\lambda \theta}}{x!} d \lambda

We can simplify this using the fact that \Gamma(h) = (h-1)! and \Gamma(h-1) = (h-2)!. This gives us:

\frac{h^{h-1}}{(h-1)!} \int^{\infty}_{0} \lambda^{h-1} e^{-\lambda (h + \theta)} \frac{(\lambda \theta)^x}{x!} d \lambda

Next, we integrate out \theta using the fact that \theta ~ generalized Pareto(\alpha, h-1, k). The probability density function for the generalized Pareto distribution is given by:

g(\theta) = \frac{(\alpha + k - 1)(\alpha + h - 1)(\alpha + x - 1)}{(\alpha - 1)(h - 1)(k - 1)} \theta^{-\alpha - 1} (1 + \frac{\theta}{k-1})^{-(\alpha + h + x)}

Therefore, the integral becomes:

\frac{h^{h-1}}{(h-1)!} \frac{(\alpha + k - 1)(\alpha + h - 1)(
 

1. What is a mixed distribution?

A mixed distribution is a probability distribution that combines two or more types of distributions. This means that the data is a mixture of different types of data, such as continuous and discrete, or normal and skewed.

2. How do you calculate the marginal distribution of X?

To calculate the marginal distribution of X, you need to sum the probabilities of X over all possible values of Y. This means you need to sum the joint probabilities of X and Y for each value of X, and then divide by the total number of observations.

3. Why is it important to show the marginal distribution of X?

The marginal distribution of X gives insight into the behavior of the variable X on its own, without considering the influence of other variables. It is helpful for understanding the overall distribution of the data and identifying any patterns or trends.

4. Can a mixed distribution be represented graphically?

Yes, a mixed distribution can be represented graphically using a histogram or a line graph. However, the shape of the graph may not be as clear as when dealing with a single distribution.

5. How does the marginal distribution of X differ from the conditional distribution of X?

The marginal distribution of X looks at the overall distribution of X without considering any other variables, while the conditional distribution of X looks at the distribution of X given a specific value or range of values for another variable.

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