986 citations · 1.3k across the 10 of their papers we have counts for
11 papers
Bayesian Computation with Intractable Likelihoods
Matthew T. Moores, Anthony N. Pettitt, Kerrie Mengersen
This article surveys computational methods for posterior inference with intractable likelihoods, that is where the likelihood function is unavailable in closed form, or where evalu…
ABC model selection for spatial extremes models applied to South Australian maximum temperature data
Xing Ju Lee, Markus Hainy, James P. McKeone +2
Max-stable processes are a common choice for modelling spatial extreme data as they arise naturally as the infinite-dimensional generalisation of multivariate extreme value theory.…
Discussion of: "A Bayesian information criterion for singular models"
N. Friel, J. P. McKeone, C. J. Oates +1
Contributed discussion to the paper of Drton and Plummer (2017), presented before the Royal Statistical Society on 5th October 2016.
Bayesian Indirect Inference Using a Parametric Auxiliary Model
Christopher C. Drovandi, Anthony N. Pettitt, Anthony Lee
Indirect inference (II) is a methodology for estimating the parameters of an intractable (generative) model on the basis of an alternative parametric (auxiliary) model that is both…
Scalable Bayesian Inference for the Inverse Temperature of a Hidden Potts Model
Matthew T. Moores, Geoff K. Nicholls, Anthony N. Pettitt +1
The inverse temperature parameter of the Potts model governs the strength of spatial cohesion and therefore has a major influence over the resulting model fit. A difficulty arises…
Investigation of the widely applicable Bayesian information criterion
N. Friel, J. P. McKeone, C. J. Oates +1
The widely applicable Bayesian information criterion (WBIC) is a simple and fast approximation to the model evidence that has received little practical consideration. WBIC uses the…
Scalable iterative methods for sampling from massive Gaussian random vectors
Daniel P. Simpson, Ian W. Turner, Christopher M. Strickland +1
Sampling from Gaussian Markov random fields (GMRFs), that is multivariate Gaussian ran- dom vectors that are parameterised by the inverse of their covariance matrix, is a fundament…
Importance Nested Sampling and the MultiNest Algorithm
F. Feroz, M. P. Hobson, E. Cameron +1
Bayesian inference involves two main computational challenges. First, in estimating the parameters of some model for the data, the posterior distribution may well be highly multi-m…
Recursive Pathways to Marginal Likelihood Estimation with Prior-Sensitivity Analysis
Ewan Cameron, Anthony Pettitt
We investigate the utility to computational Bayesian analyses of a particular family of recursive marginal likelihood estimators characterized by the (equivalent) algorithms known…
Approximate Bayesian Computation for Astronomical Model Analysis: A Case Study in Galaxy Demographics and Morphological Transformation at High Redshift
E. Cameron, A. N. Pettitt
"Approximate Bayesian Computation" (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arb…
Classification using distance nearest neighbours
Nial Friel, Anthony N. Pettitt
This paper proposes a new probabilistic classification algorithm using a Markov random field approach. The joint distribution of class labels is explicitly modelled using the dista…