Can the Adaptive Metropolis Algorithm Collapse Without the Covariance Lower Bound?

Abstract

The Adaptive Metropolis (AM) algorithm is based on the symmetric random-walk Metropolis algorithm. The proposal distribution has the following time-dependent covariance matrix, at step $n+1$<em></em>, $S_n=\mathrm{Cov}(X_1,\ldots,X_n)+\varepsilon I$,<em></em> that is, the sample covariance matrix of the history of the chain plus a (small) constant $\varepsilon&gt;0$<em> </em> multiple of the identity matrix $I$<em> </em>. The lower bound on the eigenvalues of <em>$S_n$</em> induced by the factor $\varepsilon I$<em></em> is theoretically convenient, but practically cumbersome, as a good value for the parameter <em>$\varepsilon$</em> may not always be easy to choose. This article considers variants of the AM algorithm that do not explicitly bound the eigenvalues of <em>$S_n$</em> away from zero. The behaviour of <em>$S_n$</em> is studied in detail, indicating that the eigenvalues of $S_n$<em> </em> do not tend to collapse to zero in general. In dimension one, it is shown that $S_n$<em></em> is bounded away from zero if the logarithmic target density is uniformly continuous. For a modification of the AM algorithm including an additional fixed component in the proposal distribution, the eigenvalues of <em>$S_n$</em> are shown to stay away from zero with a practically non-restrictive condition. This result implies a strong law of large numbers for super-exponentially decaying target distributions with regular contours.