The pseudo-marginal approach for efficient Monte Carlo computations

The Pseudo - Marginal Approach for Efficient Monte Carlo Computations

We introduce a powerful and flexible MCMC algorithm for stochastic simulation. The method builds on a pseudo-marginal method originally introduced in [Genetics 164 (2003) 1139–1160], showing how algorithms which are approximations to an idealized marginal algorithm, can share the same marginal stationary distribution as the idealized method. Theoretical results are given describing the converge...

On Monte Carlo and Quasi-Monte Carlo for Matrix Computations

A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design

A particle filter approach will be presented that can be used for Bayesian sequential design for random effects models in the presence of model and parameter uncertainty. Our focus is on sequential design where determining the next ‘best’ design point is defined via a utility function which could, for example, focus on parameter estimation, model discrimination or a combination of these two obj...

A New Approach for Monte Carlo Simulation of RAFT Polymerization

In this work, based on experimental observations and exact theoretical predictions, the kinetic scheme of RAFT polymerization is extended to a wider range of reactions such as irreversible intermediate radical terminations and reversible transfer reactions. The reactions which have been labeled as kinetic scheme are the more probable existing reactions as the theoretical point of view. The ...

Monte Carlo methods for matrix computations on the grid

Many scientific and engineering applications involve inverting large matrices or solving systems of linear algebraic equations. Solving these problems with proven algorithms for direct methods can take very long to compute, as they depend on the size of the matrix. The computational complexity of the stochastic Monte Carlo methods depends only on the number of chains and the length of those cha...