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Bayesian Computation with Intractable Likelihoods

Chapter


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.

UOW Authors


  •   Moores, Matt T.
  •   Pettitt, Anthony N. (external author)
  •   Mengersen, Kerrie (external author)

Publication Date


  • 2020

Citation


  • 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.

International Standard Book Number (isbn) 13


  • 9783030425524

Scopus Eid


  • 2-s2.0-85086121935

Book Title


  • Case Studies in Applied Bayesian Data Science

Start Page


  • 137

End Page


  • 151

Place Of Publication


  • Cham, Switzerland

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.

UOW Authors


  •   Moores, Matt T.
  •   Pettitt, Anthony N. (external author)
  •   Mengersen, Kerrie (external author)

Publication Date


  • 2020

Citation


  • 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.

International Standard Book Number (isbn) 13


  • 9783030425524

Scopus Eid


  • 2-s2.0-85086121935

Book Title


  • Case Studies in Applied Bayesian Data Science

Start Page


  • 137

End Page


  • 151

Place Of Publication


  • Cham, Switzerland