Chris Sherlock

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Merging MCMC Subposteriors through Gaussian-Process Approximations

Markov chain Monte Carlo (MCMC) algorithms have become powerful tools fo...
05/27/2016 ∙ by Christopher Nemeth, et al. ∙ 0 ∙ share

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Particle Metropolis-adjusted Langevin algorithms

This paper proposes a new sampling scheme based on Langevin dynamics tha...
12/23/2014 ∙ by Christopher Nemeth, et al. ∙ 0 ∙ share

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A Discrete Bouncy Particle Sampler

Markov Chain Monte Carlo (MCMC) algorithms are statistical methods desig...
07/17/2017 ∙ by Chris Sherlock, et al. ∙ 0 ∙ share

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Model-based inference of conditional extreme value distributions with hydrological applications

Multivariate extreme value models are used to estimate joint risk in a n...
05/24/2018 ∙ by Ross Towe, et al. ∙ 0 ∙ share

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Motor Unit Number Estimation via Sequential Monte Carlo

A change in the number of motor units that operate a particular muscle i...
04/11/2018 ∙ by Simon Taylor, et al. ∙ 0 ∙ share

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Simple, fast and accurate evaluation of the action of the exponential of a rate matrix on a probability vector

Given a time-homogeneous, finite-statespace Markov chain with a rate mat...
09/19/2018 ∙ by Chris Sherlock, et al. ∙ 0 ∙ share

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Efficient sampling of conditioned Markov jump processes

We consider the task of generating draws from a Markov jump process (MJP...
09/19/2018 ∙ by Andrew Golightly, et al. ∙ 0 ∙ share

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Reversible Markov chains: variational representations and ordering

This pedagogical document explains three variational representations tha...
09/06/2018 ∙ by Chris Sherlock, et al. ∙ 0 ∙ share

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Inference for extreme values under threshold-based stopping rules

There is a propensity for an extreme value analyses to be conducted as a...
01/10/2019 ∙ by Anna Maria Barlow, et al. ∙ 0 ∙ share

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Interim recruitment prediction for multi-centre clinical trials

We introduce a general framework for monitoring, modelling, and predicti...
10/17/2019 ∙ by Szymon Urbas, et al. ∙ 0 ∙ share

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Hug and Hop: a discrete-time, non-reversible Markov chain Monte Carlo algorithm

We introduced the Hug and Hop Markov chain Monte Carlo algorithm for est...
07/29/2019 ∙ by Matthew Ludkin, et al. ∙ 0 ∙ share

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Recruitment prediction for multi-centre clinical trials based on a hierarchical Poisson-gamma model: asymptotic analysis and improved intervals

We analyse predictions of future recruitment to a multi-centre clinical ...
12/20/2019 ∙ by Rachael Mountain, et al. ∙ 0 ∙ share

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Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices

We present new methodologies for Bayesian inference on the rate paramete...
12/16/2019 ∙ by Chris Sherlock, et al. ∙ 0 ∙ share

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Chris Sherlock

Featured Co-authors

Paul Fearnhead

Alexandre H. Thiery

Christopher Nemeth

Andrew Golightly

Jonathan Tawn

Matthew Ludkin

Ross Towe

Rob Lamb

Szymon Urbas

Paul Metcalfe

Anna Maria Barlow

Merging MCMC Subposteriors through Gaussian-Process Approximations

Particle Metropolis-adjusted Langevin algorithms

A Discrete Bouncy Particle Sampler

Model-based inference of conditional extreme value distributions with hydrological applications

Motor Unit Number Estimation via Sequential Monte Carlo

Simple, fast and accurate evaluation of the action of the exponential of a rate matrix on a probability vector

Efficient sampling of conditioned Markov jump processes

Reversible Markov chains: variational representations and ordering

Inference for extreme values under threshold-based stopping rules

Interim recruitment prediction for multi-centre clinical trials

Hug and Hop: a discrete-time, non-reversible Markov chain Monte Carlo algorithm

Recruitment prediction for multi-centre clinical trials based on a hierarchical Poisson-gamma model: asymptotic analysis and improved intervals

Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices