Home > Bayesian computation, Multiple parameters > Modeling Cell Phone Call Durations with a Gamma Density

Modeling Cell Phone Call Durations with a Gamma Density

Suppose we observe a sample y1, …, yn from a gamma(alpha, beta) density where the sampling density is proportional to y^{alpha-1} exp(-y/beta), and we assign a uniform prior on (alpha, beta).

As an example, suppose we wish to fit a gamma density to the durations (in minutes) of a group of cell phone calls.

12.2 0.9 0.8 5.3 2.0 1.2 1.2 1.0 0.3 1.8 3.1 2.8

Here is the R function that computes the log posterior of the density:

gamma.sampling.post1=function(theta,data)
{
a=theta[,1]
b=theta[,2]
n=length(data)
val=0*a
for (i in 1:n) val=val+dgamma(data[i],shape=a,scale=b,log=TRUE)
return(val)
}

The first figure is a contour graph of the posterior density of (alpha, beta). (In R, beta is called the scale parameter.)
Note the strong curvature in the posterior.

Instead, suppose we consider the joint posterior of alpha and the “rate” parameter theta = 1/beta. Here is a contour plot of the posterior of (alpha, theta).

This doesn’t display the strong curvature.

Last, suppose you consider the joint posterior of alpha and the mean mu = alpha beta. The last figure displays the posterior of (alpha, mu).The moral here is that the choice of parameterization can be important when summarizing the posterior distribution. In the next chapter, we’ll suggest a rule of thumb for transforming parameters that makes it easier to summarize many posteriors.

Advertisements
  1. No comments yet.
  1. No trackbacks yet.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: