Acceleration of the EM algorithm
The EM algorithm is used for many applications including Boltzmann machine, stochastic Perceptron and HMM. This algorithm gives an iterating procedure for calculating the MLE of stochastic models which have hidden random variables. It is simple, but the convergence is slow. We also have “Fisher’s scoring method”. Its convergence is faster, but the calculation is heavy. We show that by using the EM algorithm recursively, we can connect these two methods and accelerate the EM algorithm. Also Louis, Meng and Rubin showed they can accelerate the EM algorithm, but our algorithm is simpler. We show some numerical simulations with our algorithm.