RIS ID

143087

Publication Details

Moores, M. T., Pettitt, A. N. & Mengersen, K. L. (2020). Bayesian Computation with Intractable Likelihoods. In K. L. Mengersen, P. Pudlo & C. P. Robert (Eds.), Case Studies in Applied Bayesian Data Science (pp. 137-151). Cham, Switzerland: Springer.

Abstract

This article surveys computational methods for posterior inference with intractable likelihoods, that is where the likelihood function is unavailable in closed form, or where evaluation of the likelihood is infeasible. We review recent developments in pseudo-marginal methods, approximate Bayesian computation (ABC), the exchange algorithm, thermodynamic integration, and composite likelihood, paying particular attention to advancements in scalability for large datasets. We also mention R and MATLAB source code for implementations of these algorithms, where they are available.

Available for download on Saturday, May 28, 2022

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/978-3-030-42553-1_6