Description
Study of Expectation Maximization (EM) algorithm
The objective of this exercise is to understand the Expectation Maximization (EM) algorithm. Assume that there are two biased coins, labelled as A and B. Coin A lands up as head with probability p and coin B lands up as head with probability q. An experiment is performed with n independent trials and in each trial, a coin is chosen at random (coin A with probability π and coin B with probability 1 − π) and tossed m times (independently).
Let z(i) ∈ {A,B} be the label of the coin selected and x(i) ∈ {H,T}m be the observed sequence in the ith trial of the experiment, i ∈ 1,2,…,n. The labels of the coins {z(i),i ∈ 1,2,…,n} remain hidden and the observer could only record the faces {x(i),i ∈ 1,2,…,n}. Hence, the observations x(i) can be considered as i.i.d samples from a Mixture of two Binomial models:
PX(x) = π × PX(x|z(i) = A) + (1 − π) × PX(x|z(i) = B)
Our aim is to obtain maximum likelihood estimate for parameter vector θ using Expectation Maximization (EM) algorithm. Generate the observations (x(i),z(i)) for following two cases
(i) π = 0.50, p = 0.35 and q = 0.60. (ii) π = 0.25, p = 0.35 and q = 0.60.
Choose m = 1,10 and n = 10,1000,10000. Run the EM algorithm for following experiments
Experiment: 1 Assume that we know π. Thus, the vector of parameters is given by θ = [p q]T.
- Use observations from case (i) and assume that π = 0.50 and initial estimates are [0.45 0.50]T
- Use observations from case (ii) and assume that π = 0.50 and initial estimates are [0.45 0.50]T
- Use observations from case (ii) and assume that π = 0.25 and initial estimates are [0.45 0.50]T
Experiment: 2 Assume that we do not know π. Thus, the vector of parameters is given by θ = [π p q]T.
- Use observations from case (ii) and initial estimates are [0.50 0.45 0.50]T
- Repeat above experiment with initial estimate [0.50 0.50 0.45]T
Experiment: 3 Now use a Beta prior over π. Derive the update equations for MAP estimate. Use the following priors Beta(1,1),Beta(1,3),Beta(2,6),Beta(3,9).
Make the following inferences from the algorithm for the aforementioned choices of n, m and θˆ0:
1
- Plot the learning curve[1] and show the convergence of the algorithm[2] for each experiment.
- Observe how the final estimate of θ from the algorithm (call it θˆEM), and number of iterations needed for convergence, change when we increase m and n.
- Report your observation about case (b) and case (c) of experiment 1, where in former case we are assuming a wrong value of π and in later case we are assuming the true value of π
- Report your observation about experiment 2 when we swap the prior for p and q.
- Compare the result of experiment 3 with the result of experiment 2.
[1] Plot of the estimate at iteration k, θˆk vs. iteration index, k.
[2] You shall consider that the algorithm has converged at iteration k when the update to any of the parameter is not more than
