Distribution of exponential family

In summary, the conversation discusses the probability function for a 2-vector random variable and its relation to the exponential family. The goal is to find the expected value and variance matrices, and help is needed in bringing the function to canonical form.
  • #1
the_dane
30
0
Let's say my probability function is given by: p(y1,y2)=Γ(y1+y2+γ)/((y1+y2)!*Γ(γ)), when γ>0 is known. I suppose it is from an exponential family but I can't write in canonical form because I'm only familiar with exponential family with one variable so I'm confused now when there's to variable. Can someone help me out here.
 
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  • #2
For the distribution of a 2-vector random variable like that to be in the exponential family it must be able to be written in the factorised form

$$p(y_1,y_2)=h(y_1,y_2)g(\gamma)\exp\left(\sum_{k=1}^s\eta_k(\gamma)T_k(y_1,y_2)\right)$$

where ##s## is a non-negative integer and ##h,g, \eta_1,...,\eta_s, T_1,...,T_s## are all known functions.
 
  • #3
the_dane said:
Let's say my probability function is given by: p(y1,y2)=Γ(y1+y2+γ)/((y1+y2)!*Γ(γ)), when γ>0 is known. I suppose it is from an exponential family but I can't write in canonical form because I'm only familiar with exponential family with one variable so I'm confused now when there's to variable. Can someone help me out here.
Consider the random variable ##Z=Y_1+Y_2##.

Therefore, you have:

##
\begin{eqnarray*}
\frac{\Gamma(y_1+y_2+\gamma)}{(y_1+y_2)! \ \Gamma(\gamma)} = \frac{\Gamma(z+\gamma)}{z! \ \Gamma(\gamma)} = \frac{1}{z!} \cdot \frac{1}{\Gamma(\gamma)} \cdot \Gamma(z+\gamma) \\
\end{eqnarray*}##

Do you have a function ##h(z)## and a function ##g(\gamma)## now?

Also, remember that ##a=\exp(\log(a))##! :wink:
 
  • #4
Thanks for the answers. I have edited my model a lot and I'm now looking at this model. probability function for Y=(Y1,Y2) is given by p. Can anyone bring this to canonical form so I can find expected value and variance matrixes.
https://dl.dropboxusercontent.com/u/17974596/Sk%C3%A6rmbillede%202016-02-02%20kl.%2007.35.26.png
 
Last edited by a moderator:
  • #5
... or just help me find the variance and expected value :)
 

Related to Distribution of exponential family

1. What is the exponential family distribution?

The exponential family distribution is a family of probability distributions that can be written in a specific form, which includes a natural parameter and a sufficient statistic. The most common types of distributions that fall under this family are the normal, gamma, and Poisson distributions.

2. What are the properties of the exponential family distribution?

The exponential family distribution has several important properties, including conjugacy, sufficiency, and completeness. Conjugacy means that the posterior distribution is in the same family as the prior distribution. Sufficiency means that the sufficient statistic contains all the necessary information about the parameter. Completeness means that the expected value of the sufficient statistic is equal to the parameter.

3. What are some applications of the exponential family distribution?

The exponential family distribution is commonly used in statistical modeling and inference. It is often used to model data that follow a specific pattern, such as counts (Poisson distribution) or continuous data (normal distribution). It is also used in machine learning algorithms, such as linear regression and logistic regression.

4. How do you determine which distribution belongs to the exponential family?

To determine if a distribution belongs to the exponential family, you can check if it can be written in the form of $f(x;\theta) = h(x)c(\theta)e^{(\phi(\theta)T(x))}$, where $h(x)$ is the base measure, $c(\theta)$ is the normalization constant, $\phi(\theta)$ is the natural parameter, and $T(x)$ is the sufficient statistic. If a distribution can be written in this form, it belongs to the exponential family.

5. What is the importance of the exponential family distribution in statistics?

The exponential family distribution has many important applications in statistics, including being used as a building block for more complex models. It has useful properties, such as conjugacy, which makes it easier to perform statistical inference. It is also widely applicable, with many commonly used distributions belonging to this family, making it a useful tool for data analysis and modeling.

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