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Theoretical and Methodological Aspects of Markov Chain Monte Carlo Computations with Noisy Likelihoods
| Content Provider | Scilit |
|---|---|
| Author | Andrieu, Christophe Lee, Anthony Vihola, Matti |
| Copyright Year | 2018 |
| Description | Book Name: Handbook of Approximate Bayesian Computation |
| Abstract | Approximate Bayesian computation (ABC) is a popular method that consists of defining an alternative likelihood function, which is also in general intractable, but naturally lends itself to pseudo-marginal computations, hence, making the approach of practical interest. This chapter shows the connections of ABC Markov chain Monte Carlo (MCMC) with pseudo-marginal algorithms, reviews their existing theoretical results, and discusses how these can inform practice and hopefully lead to fruitful methodological developments. It describes standard performance measures for MCMC algorithms and a summary of some known theoretical results relating the properties of to the performance of pseudo-marginal algorithms. The chapter considers the comparing different variations of the noisy algorithm. It presents a relevant subset of theory and directions in methodological research pertaining to ABC-MCMC algorithms. The chapter suggests that when the model admits specific structure, alternatives to the simple ABC method presented here may be more computationally efficient.Approximate Bayesian computation (ABC) is a popular method that consists of defining an alternative likelihood function, which is also in general intractable, but naturally lends itself to pseudo-marginal computations, hence, making the approach of practical interest. This chapter shows the connections of ABC Markov chain Monte Carlo (MCMC) with pseudo-marginal algorithms, reviews their existing theoretical results, and discusses how these can inform practice and hopefully lead to fruitful methodological developments. It describes standard performance measures for MCMC algorithms and a summary of some known theoretical results relating the properties of to the performance of pseudo-marginal algorithms. The chapter considers the comparing different variations of the noisy algorithm. It presents a relevant subset of theory and directions in methodological research pertaining to ABC-MCMC algorithms. The chapter suggests that when the model admits specific structure, alternatives to the simple ABC method presented here may be more computationally efficient. |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2011-0-05316-1&isbn=9781315117195&format=googlePreviewPdf |
| Ending Page | 268 |
| Page Count | 26 |
| Starting Page | 243 |
| DOI | 10.1201/9781315117195-9 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2018-09-03 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Handbook of Approximate Bayesian Computation History and Philosophy of Science Algorithms Mcmc Chain Monte Carlo Pseudo Marginal Markov Chain Monte |
| Content Type | Text |
| Resource Type | Chapter |
