  We propose a sequential Markov chain Monte Carlo (SMCMC) algorithm to sample
from a sequence of probability distributions, corresponding to posterior
distributions at different times in on-line applications. SMCMC proceeds as in
usual MCMC but with the stationary distribution updated appropriately each time
new data arrive. SMCMC has advantages over sequential Monte Carlo (SMC) in
avoiding particle degeneracy issues. We provide theoretical guarantees for the
marginal convergence of SMCMC under various settings, including parametric and
nonparametric models. The proposed approach is compared to competitors in a
simulation study. We also consider an application to on-line nonparametric
regression.
