Abstract:
The Metropolis-Hastings Markov Chain
Monte Carlo algorithm uses a proposal distribution to sample from a given target distribution. The proposal distribution has to be
tuned beforehand which is an expensive exercise. However, adaptive algorithms automatically tune the proposal distribution based on
knowledge from past samples, reducing the
tuning cost. In this study we first examine
Adaptive Metropolis, Metropolis Gaussian
Adaptation and Covariance Matrix Adaptation Evolutionary Strategies. The latter is
usually used as an optimization strategy but
it is feasible to incorporate it in MCMC.
