63 citations · 531 across the 74 of their papers we have counts for
13 papers · 1 filter
SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks
Edgar Santos-Fernandez, Jay M. Ver Hoef, James M. McGree +3
Spatio-temporal models are widely used in many research areas from ecology to epidemiology. However, most covariance functions describe spatial relationships based on Euclidean dis…
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…
Improved assessment of the accuracy of record linkage via an extended MaCSim approach
Shovanur Haque, Kerrie Mengersen
Record linkage is the process of bringing together the same entity from overlapping data sources while removing duplicates. Huge amounts of data are now being collected by public o…
Assessing the accuracy of individual link with varying block sizes and cut-off values using MaCSim approach
Shovanur Haque, Kerrie Mengersen
Record linkage is the process of matching together records from different data sources that belong to the same entity. Record linkage is increasingly being used by many organizatio…
Combined parameter and state inference with automatically calibrated ABC
Anthony Ebert, Pierre Pudlo, Kerrie Mengersen +2
State space models contain time-indexed parameters, termed states, as well as static parameters, simply termed parameters. The problem of inferring both static parameters as well a…
Assessing the accuracy of record linkages with Markov chain based Monte Carlo simulation approach
Shovanur Haque, Kerrie Mengersen, Steven Stern
Record linkage is the process of finding matches and linking records from different data sources so that the linked records belong to the same entity. There is an increasing number…
Approximating the Likelihood in Approximate Bayesian Computation
Christopher C Drovandi, Clara Grazian, Kerrie Mengersen +1
This chapter will appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). The conceptual and methodological framework that underpins approximate Bayesian com…
Computationally Efficient Simulation of Queues: The R Package queuecomputer
Anthony Ebert, Paul Wu, Kerrie Mengersen +1
Large networks of queueing systems model important real-world systems such as MapReduce clusters, web-servers, hospitals, call centers and airport passenger terminals. To model suc…
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…
Pre-processing for approximate Bayesian computation in image analysis
Matthew T. Moores, Christopher C. Drovandi, Kerrie Mengersen +1
Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computa…
Bayesian computation via empirical likelihood
K. L. Mengersen, P. Pudlo, C. P. Robert
Approximate Bayesian computation (ABC) has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However,…
Exact Bayesian Analysis of Mixtures
Christian P. Robert, Kerrie L. Mengersen
In this paper, we show how a complete and exact Bayesian analysis of a parametric mixture model is possible in some cases when components of the mixture are taken from exponential…
Bayesian Inference on Mixtures of Distributions
Kate Lee, Jean-Michel Marin, Kerrie Mengersen +1
This survey covers state-of-the-art Bayesian techniques for the estimation of mixtures. It complements the earlier Marin, Mengersen and Robert (2005) by studying new types of distr…