Markov chain Monte Carlo (MCMC) algorithms provide a very general recipe for estimating properties of complicated distributions. While their use has become commonplace and there is a large literature on MCMC theory and practice, MCMC users still have to contend with several challenges with each implementation of the algorithm. These challenges include determining how to construct an efficient a...

This paper investigates the potential of a cellular automata (CA) model based on logistic regression (logit) and Markov Chain Monte Carlo (MCMC) to simulate the dynamics of urban growth. The model assesses urbanization likelihood based on (i) a set of urban development driving forces (calibrated based on logit) and (ii) the land-use of neighboring cells (calibrated based on MCMC). An innovative...

We implement a parallel MCMC method based on the ensemble samplers proposed by Jonathan Goodman and Jonathan Weare [1]. The new algorithm has several advantages over standard MCMC method. We made some numerical experiments and test the efficiency and strong/weak scalability of the parallel method. The parallel algorithm we implement is based on the MCMC hammer [2]. 0.

We review recent work concerning optimal proposal scalings for Metropolis-Hastings MCMC algorithms, and adaptive MCMC algorithms for trying to improve the algorithm on the fly.

In order to gain an understanding of the effectiveness of phylogenetic Markov chain Monte Carlo (MCMC), it is important to understand how quickly the empirical distribution of the MCMC converges to the posterior distribution. In this article, we investigate this problem on phylogenetic tree topologies with a metric that is especially well suited to the task: the subtree prune-and-regraft (SPR) ...

Abstract The use of heuristics to assess the convergence and compress output Markov chain Monte Carlo can be sub-optimal in terms empirical approximations that are produced. Typically a number initial states attributed ‘burn in’ removed, while remainder is ‘thinned’ if compression also required. In this paper, we consider problem retrospectively selecting subset states, fixed cardinality, from ...

Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent methods have been used enhance autoencoders, need robust uncertainty quantification remains a challenge. This has addressed with variational autoencoders so far. Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling faced several ...