# Eleisha's Segment 27: Mixture Models

To Calculate:

The file Media:Mixturevals.txt contains 1000 values, each drawn either with probability c from the distribution ${\text{Exponential}}(\beta )$ (for some constant $\beta$ ), or otherwise (with probability $1-c$ ) from the distribution $p(x)=(2/\pi )/(1+x^{2}),\;x>0$ .

1. Write down an expression for the probability of the file's data given some values for the parameters $\beta$ and $c$ .

2. Calculate numerically the maximum likelihood values of $\beta$ and $c$ .

3. Estimate numerically the Bayes posterior distribution of $\beta$ , marginalizing over c as a nuisance parameter. (You'll of course have to make some assumption about priors.)

1. In problem 3, above, you assumed some definite prior for $c$ . What if $c$ is itself drawn (just once for the whole data set) from a distribution ${\text{Beta}}(\mu ,\nu )$ , with unknown hyperparameters $\mu ,\nu$ . How would you now estimate the Bayes posterior distribution of $\beta$ , marginalizing over everything else?