  A novel procedure is described for accelerating the convergence of Markov
chain Monte Carlo computations. The algorithm uses an adaptive bootstrap
technique to generate candidate steps in the Markov Chain. It is efficient for
symmetric, convex probability distributions, similar to multivariate Gaussians,
and it can be used for Bayesian estimation or for obtaining maximum likelihood
solutions with confidence limits. As a test case, the Law of Categorical
Judgment (Corrected) was fitted with the algorithm to data sets from simulated
rating scale experiments. The correct parameters were recovered from
practical-sized data sets simulated for Full Signal Detection Theory and its
special cases of standard Signal Detection Theory and Complementary Signal
Detection Theory.
