A Sequential Monte Carlo Algorithm to Incorporate Model Uncertainty in Bayesian Sequential Design

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...

Sequential Monte Carlo for Bayesian Computation

Sequential Monte Carlo (SMC) methods are a class of importance sampling and resampling techniques designed to simulate from a sequence of probability distributions. These approaches have become very popular over the last few years to solve sequential Bayesian inference problems (e.g. Doucet et al. 2001). However, in comparison to Markov chain Monte Carlo (MCMC), the application of SMC remains l...

Bayesian Optimization Using Sequential Monte Carlo

We consider the problem of optimizing a real-valued continuous function f using a Bayesian approach, where the evaluations of f are chosen sequentially by combining prior information about f , which is described by a random process model, and past evaluation results. The main difficulty with this approach is to be able to compute the posterior distributions of quantities of interest which are u...

Sequential Monte Carlo Samplers

In this paper, we propose a methodology to sample sequentially from a sequence of probability distributions known up to a normalizing constant and defined on a common space. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time using Sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make para...

Sequential Monte Carlo Workshop