# Segment 27. Mixture Models

## Contents

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### Problems

#### 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?