N
Nicola G. Best
Researcher at Imperial College London
Publications - 21
Citations - 14212
Nicola G. Best is an academic researcher from Imperial College London. The author has contributed to research in topics: Markov chain Monte Carlo & Bayesian probability. The author has an hindex of 12, co-authored 17 publications receiving 12416 citations.
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Bayesian measures of model complexity and fit
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
Journal Article
Bayesian measures of model complexity and fit
TL;DR: The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages.
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The deviance information criterion: 12 years on
TL;DR: In this paper, the essentials of our paper of 2002 are briefly summarized and compared with other criteria for model comparison, after some comments on the paper's reception and influence, we consider criticisms and proposals forimprovement made by us and others.
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A shared component model for detecting joint and selective clustering of two diseases.
TL;DR: A shared component model is proposed for the joint spatial analysis of two diseases to separate the underlying risk surface for each disease into a shared and a disease‐specific component.
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Dynamic conditional independence models and Markov chain Monte Carlo methods
TL;DR: The proposed dynamic sampling algorithms use posterior samples from previous updating stages and exploit conditional independence between groups of parameters to allow samples of parameters no longer of interest to be discarded, such as when a patient dies or is discharged.