Gibbs sampling is a widely applicable inference technique that can in principle deal with complex multimodal distributions. Unfortunately, it fails in many practical applications due to slow convergence and abundance of local minima. In this paper, we propose a general method of speeding up Gibbs sampling in probabilistic models. The method works by introducing auxiliary variables which represe...

Quantum Annealing (QA) was originally intended for accelerating the solution of combinatorial optimization tasks that have natural encodings as Ising models. However, recent experiments on QA hardware platforms demonstrated that, in operating regime corresponding to weak interactions, behaves like a noisy Gibbs sampler at hardware-specific effective temperature. This work builds those insights ...

Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding widespread use in applied statistics and machine learning. These often require significant computational power, and are increasingly being deployed on parallel and distributed systems such as compute clusters. Recent work has proposed running iterative algorithms such as gradient descent and MCMC in parallel asynchronous...

Inference for belief networks using Gibbs sampling produces a distribution for unob­ served variables that differs from the correct distribution by a (usually) unknown error, since convergence to the right distribution occurs only asymptotically. The method of "coupling from the past" samples from ex­ actly the correct distribution by ( conceptu­ ally) running dependent Gibbs sampling sim­ ulat...

A new method of discovering the common secondary structure of a family of homologous RNA sequences using Gibbs sampling and stochastic context-free grammars is proposed. Given an unaligned set of sequences, a Gibbs sampling step simultaneously estimates the secondary structure of each sequence and a set of statistical parameters describing the common secondary structure of the set as a whole. T...

The objective of this paper is to provide readers with the program to estimate a Markov switching model with time varying transition probability(Filardo, 1994) by using a statistical computing software R. Although many of the previous studies estimating the model have conducted the estimation by the maximum likelihood estimation, this paper utilizes Gibbs sampling method. Using Gibbs sampling m...