Bayesian experimental design is a fast growing area of research with many real‐world
applications. As computational power has increased over the years, so has the development …

We estimate the parameters of a stochastic process model for a macroparasite population
within a host using approximate Bayesian computation (ABC). The immunity of the host is an …

Having the ability to work with complex models can be highly beneficial. However, complex
models often have intractable likelihoods, so methods that involve evaluation of the …

In this paper, we present new multivariate quantile distributions and utilise likelihood-free
Bayesian algorithms for inferring the parameters. In particular, we apply a sequential Monte …

We present a novel approach for developing summary statistics for use in approximate
Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are …

Bayesian Indirect Inference Using a Parametric Auxiliary Model Page 1 Statistical Science
2015, Vol. 30, No. 1, 72–95 DOI: 10.1214/14-STS498 © Institute of Mathematical Statistics …

The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based
inference approach to quantifying and interpreting effects, and in a case study example …

This article presents a sequential Monte Carlo (SMC) algorithm that can be used for any one-
at-a-time Bayesian sequential design problem in the presence of model uncertainty where …

The understanding of complex physical or biological systems nearly always requires a
characterization of the variability that underpins these processes. In addition, the data used …

Big Datasets are endemic, but are often notoriously difficult to analyse because of their size,
heterogeneity and quality. The purpose of this paper is to open a discourse on the potential …