Weak convergence and optimal scaling of random walk Metropolis algorithms
- G. Roberts, A. Gelman, W. Gilks
- Mathematics
- 1 February 1997
This paper considers the problem of scaling the proposal distribution of a multidimensional random walk Metropolis algorithm in order to maximize the efficiency of the algorithm. The main result is a…
Highly Conserved Non-Coding Sequences Are Associated with Vertebrate Development
- Adam Woolfe, Martin Goodson, G. Elgar
- BiologyPLoS Biology
- 11 November 2004
A whole-genome comparison between humans and the pufferfish, Fugu rubripes, is used to identify nearly 1,400 highly conserved non-coding sequences, which are likely to form part of the genomic circuitry that uniquely defines vertebrate development.
A Language and Program for Complex Bayesian Modelling
- W. Gilks, A. Thomas, D. Spiegelhalter
- Computer Science
- 1 March 1994
This work describes some general purpose software that is currently developing for implementing Gibbs sampling: BUGS (Bayesian inference using Gibbs sampling), written in Modula-2 and runs under both DOS and UNIX.
Markov Chain Monte Carlo in Practice
- W. Gilks, S. Richardson, D. Spiegelhalter
- Computer Science
- 1 August 1997
The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
BUGS - Bayesian inference Using Gibbs Sampling Version 0.50
- D. Spiegelhalter, Andrew Thomas, N. G. Best, W. Gilks
- Computer Science
- 1995
Following a moving target—Monte Carlo inference for dynamic Bayesian models
- W. Gilks, C. Berzuini
- Computer Science
- 2001
This work proposes a new technique for tracking moving target distributions, known as particle filters, which does not suffer from a progressive degeneration as the target sequence evolves.
Adaptive Rejection Sampling for Gibbs Sampling
SUMMARY We propose a method for rejection sampling from any univariate log-concave probability density function. The method is adaptive: as sampling proceeds, the rejection envelope and the squeezing…
Adaptive Rejection Metropolis Sampling Within Gibbs Sampling
A robust nonlinear full probability model for population pharmacokinetic data is proposed and it is demonstrated that the method enables Bayesian inference for this model, through an analysis of antibiotic administration in new‐born babies.
Inference and monitoring convergence
- W. Gilks, S. Richardson, D. Spiegelhalter
- Computer Science
- 1 December 1995
Adaptive Markov Chain Monte Carlo through Regeneration
- W. Gilks, G. Roberts, S. Sahu
- Mathematics
- 1 September 1998
Abstract Markov chain Monte Carlo (MCMC) is used for evaluating expectations of functions of interest under a target distribution π. This is done by calculating averages over the sample path of a…
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