Eleisha's Segment 27: Mixture Models
The file Media:Mixturevals.txt contains 1000 values, each drawn either with probability c from the distribution (for some constant ), or otherwise (with probability ) from the distribution .
1. Write down an expression for the probability of the file's data given some values for the parameters and .
2. Calculate numerically the maximum likelihood values of and .
3. Estimate numerically the Bayes posterior distribution of , marginalizing over c as a nuisance parameter. (You'll of course have to make some assumption about priors.)
To Think About:
1. In problem 3, above, you assumed some definite prior for . What if is itself drawn (just once for the whole data set) from a distribution , with unknown hyperparameters . How would you now estimate the Bayes posterior distribution of , marginalizing over everything else?
Back To: Eleisha Jackson