A review of approximate Bayesian computation methods via density estimation: Inference for simulator‐models

Grazian, Clara; Fan, Yanan

doi:10.1002/wics.1486

Cited by 17 publications

(13 citation statements)

References 69 publications

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“…Our method integrates strong points of both approximation and simulation based approaches, and it is both scalable and extendable to non-regular lattices which are important for real applications. Our procedure also shares some connections with the literature on approximate Bayesian computation (Sisson et al 2018), see for example Fan et al (2014) and Grazian and Fan (2019), where the intractable likelihoods have been replaced by flexible density approximations. The Matlab code for our method is available at Github.…”

Section: Introductionmentioning

confidence: 80%

A synthetic likelihood approach for intractable markov random fields

Zhu

Fan

2022

Comput Stat

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We propose a new scalable method to approximate the intractable likelihood of the Potts model. The method decomposes the original likelihood into products of many low-dimensional conditional terms, and a Monte Carlo method is then proposed to approximate each of the small terms using their corresponding (exact) Multinomial distribution. The resulting tractable synthetic likelihood then serves as an approximation to the true likelihood. The method is scalable with respect to lattice size and can also be used for problems with irregular lattices. We provide theoretical justifications for our approach, and carry out extensive simulation studies, which show that our method performs at least as well as existing methods, whilst providing significant computational savings, up to ten times faster than the current fastest method. Finally, we include three real data applications for illustration.

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Section: Introductionmentioning

confidence: 80%

A synthetic likelihood approach for intractable markov random fields

Zhu

Fan

2022

Comput Stat

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“…As future work, one could study more closely how to best use these methods for specific models and inference tasks. One could also investigate if LFI techniques such as regression adjustment (Beaumont et al, 2002;Blum, 2010), Bayesian synthetic likelihood (Price et al, 2018) or conditional density estimation methods based on neural networks or Gaussian processes (Papamakarios and Murray, 2016;Papamakarios et al, 2019;Grazian and Fan, 2020;Järvenpää et al, 2020) can be used to improve the accuracy or computational efficiency of the ABC-P approach. Another important research direction would be to study how to construct informative summary statistics for predictive ABC inference in an automatic fashion.…”

Section: Discussionmentioning

confidence: 99%

On predictive inference for intractable models via approximate Bayesian computation

Järvenpää¹,

Corander²

2022

Preprint

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Approximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based models whose likelihood function cannot be evaluated. In this paper we instead investigate the feasibility of ABC as a generic approximate method for predictive inference, in particular, for computing the posterior predictive distribution of future observations or missing data of interest. We consider three complementary ABC approaches for this goal, each based on different assumptions regarding which predictive density of the intractable model can be sampled from. The case where only simulation from the joint density of the observed and future data given the model parameters can be used for inference is given particular attention and it is shown that the ideal summary statistic in this setting is minimal predictive sufficient instead of merely minimal sufficient (in the ordinary sense). An ABC prediction approach that takes advantage of a certain latent variable representation is also investigated. We additionally show how common ABC sampling algorithms can be used in the predictive settings considered. Our main results are first illustrated by using simple timeseries models that facilitate analytical treatment, and later by using two common intractable dynamic models.

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“…Recently, density estimation techniques based on deep neural network architectures have been developed for likelihood-free inference [Grazian and Fan, 2019, Lueckmann et al, 2018, Papamakarios et al, 2019. These approaches are similar to the SL-type methods where the likelihood is approximated with a parametric surrogate, but in the place of the parametric distribution, neural network architectures such as autoregressive flows or emulator networks are used to fit a surrogate model for the likelihood.…”

Section: Recent Progress In Methods Developmentmentioning

confidence: 99%

“…Nevertheless, the generic application potential of ABC and other likelihood-free inference (LFI) methods has been held back by the computational requirements of its standard inference algorithms and the lack of a suitable all-purpose software implementation. With the advent of more efficient inference strategies adopted from the field of machine learning [Gutmann and Corander, 2016, Lueckmann et al, 2018, Thomas et al, 2021, Kokko et al, 2019, Cranmer et al, 2020, Grazian and Fan, 2019, Papamakarios et al, 2019 and software platforms such as Engine for likelihood-free inference (ELFI) [Lintusaari et al, 2018], ABCpy , BSL [An et al, 2019a] and sbi [Tejero-Cantero et al, 2020], to name a few, the immediate prospect of both using and updating the ABC/LFI toolkits for challenging real-world applications certainly looks brighter.…”

Section: Introductionmentioning

confidence: 99%

ABC of the Future

Pesonen¹,

Simola²,

Köhn‐Luque³

et al. 2021

Preprint

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Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are becoming increasingly popular in many research domains. The computational feasibility of ABC for practical applications has been recently boosted by adopting techniques from machine learning to build surrogate models for the approximate likelihood or posterior and by the introduction of a general-purpose software platform with several advanced features, including automated parallelization. Here we demonstrate the strengths of the advances in ABC by going beyond the typical benchmark examples and considering real applications in astronomy, infectious disease epidemiology, personalised cancer therapy and financial prediction. We anticipate that the emerging success of ABC in producing actual added value and quantitative insights in the real world will continue to inspire a plethora of further applications across different fields of science, social science and technology.

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A review of approximate Bayesian computation methods via density estimation: Inference for simulator‐models

Cited by 17 publications

References 69 publications

A synthetic likelihood approach for intractable markov random fields

A synthetic likelihood approach for intractable markov random fields

On predictive inference for intractable models via approximate Bayesian computation

ABC of the Future

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