A rare event approach to high-dimensional approximate Bayesian computation
Approximate Bayesian computation (ABC) methods permit approximate inference for intractable likelihoods when it is possible to simulate from the model. However they perform poorly for high dimensional data, and in practice must usually be used in conjunction with dimension reduction methods, resulti...
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| Format: | Article |
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Springer
2017
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| Online Access: | https://eprints.nottingham.ac.uk/44045/ |
| _version_ | 1848796823943118848 |
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| author | Prangle, Dennis Everitt, Richard G. Kypraios, Theodore |
| author_facet | Prangle, Dennis Everitt, Richard G. Kypraios, Theodore |
| author_sort | Prangle, Dennis |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Approximate Bayesian computation (ABC) methods permit approximate inference for intractable likelihoods when it is possible to simulate from the model. However they perform poorly for high dimensional data, and in practice must usually be used in conjunction with dimension reduction methods, resulting in a loss of accuracy which is hard to quantify or control. We propose a new ABC method for high dimensional data based on rare event methods which we refer to as RE-ABC. This uses a latent variable representation of the model. For a given parameter value, we estimate the probability of the rare event that the latent variables correspond to data roughly consistent with the observations. This is performed using sequential Monte Carlo and slice sampling to systematically search the space of latent variables. In contrast standard ABC can be viewed as using a more naïve Monte Carlo estimate. We use our rare event probability estimator as a likelihood estimate within the pseudo-marginal Metropolis-Hastings algorithm for parameter inference. We provide asymptotics showing that RE-ABC has a lower computational cost for high dimensional data than standard ABC methods. We also illustrate our approach empirically, on a Gaussian distribution and an application in infectious disease modelling. |
| first_indexed | 2025-11-14T19:54:07Z |
| format | Article |
| id | nottingham-44045 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:54:07Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-440452020-05-04T18:54:58Z https://eprints.nottingham.ac.uk/44045/ A rare event approach to high-dimensional approximate Bayesian computation Prangle, Dennis Everitt, Richard G. Kypraios, Theodore Approximate Bayesian computation (ABC) methods permit approximate inference for intractable likelihoods when it is possible to simulate from the model. However they perform poorly for high dimensional data, and in practice must usually be used in conjunction with dimension reduction methods, resulting in a loss of accuracy which is hard to quantify or control. We propose a new ABC method for high dimensional data based on rare event methods which we refer to as RE-ABC. This uses a latent variable representation of the model. For a given parameter value, we estimate the probability of the rare event that the latent variables correspond to data roughly consistent with the observations. This is performed using sequential Monte Carlo and slice sampling to systematically search the space of latent variables. In contrast standard ABC can be viewed as using a more naïve Monte Carlo estimate. We use our rare event probability estimator as a likelihood estimate within the pseudo-marginal Metropolis-Hastings algorithm for parameter inference. We provide asymptotics showing that RE-ABC has a lower computational cost for high dimensional data than standard ABC methods. We also illustrate our approach empirically, on a Gaussian distribution and an application in infectious disease modelling. Springer 2017-07-11 Article PeerReviewed Prangle, Dennis, Everitt, Richard G. and Kypraios, Theodore (2017) A rare event approach to high-dimensional approximate Bayesian computation. Statistics and Computing, 28 (4). pp. 819-834. ISSN 1573-1375 ABC Markov chain Monte Carlo Sequential Monte Carlo Slice sampling Infectious disease modelling https://link.springer.com/article/10.1007/s11222-017-9764-4 doi:10.1007/s11222-017-9764-4 doi:10.1007/s11222-017-9764-4 |
| spellingShingle | ABC Markov chain Monte Carlo Sequential Monte Carlo Slice sampling Infectious disease modelling Prangle, Dennis Everitt, Richard G. Kypraios, Theodore A rare event approach to high-dimensional approximate Bayesian computation |
| title | A rare event approach to high-dimensional approximate Bayesian computation |
| title_full | A rare event approach to high-dimensional approximate Bayesian computation |
| title_fullStr | A rare event approach to high-dimensional approximate Bayesian computation |
| title_full_unstemmed | A rare event approach to high-dimensional approximate Bayesian computation |
| title_short | A rare event approach to high-dimensional approximate Bayesian computation |
| title_sort | rare event approach to high-dimensional approximate bayesian computation |
| topic | ABC Markov chain Monte Carlo Sequential Monte Carlo Slice sampling Infectious disease modelling |
| url | https://eprints.nottingham.ac.uk/44045/ https://eprints.nottingham.ac.uk/44045/ https://eprints.nottingham.ac.uk/44045/ |