Literature DB >> 32471948

The frontier of simulation-based inference.

Kyle Cranmer1,2, Johann Brehmer3,2, Gilles Louppe4.   

Abstract

Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving additional momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound influence these developments may have on science.

Keywords:  approximate Bayesian computation; implicit models; likelihood-free inference; neural density estimation; statistical inference

Year:  2020        PMID: 32471948      PMCID: PMC7720103          DOI: 10.1073/pnas.1912789117

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  8 in total

1.  Markov chain Monte Carlo without likelihoods.

Authors:  Paul Marjoram; John Molitor; Vincent Plagnol; Simon Tavare
Journal:  Proc Natl Acad Sci U S A       Date:  2003-12-08       Impact factor: 11.205

2.  Approximate Bayesian computation in population genetics.

Authors:  Mark A Beaumont; Wenyang Zhang; David J Balding
Journal:  Genetics       Date:  2002-12       Impact factor: 4.562

3.  Sequential Monte Carlo without likelihoods.

Authors:  S A Sisson; Y Fan; Mark M Tanaka
Journal:  Proc Natl Acad Sci U S A       Date:  2007-01-30       Impact factor: 11.205

Review 4.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

5.  Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions.

Authors:  Hongqiao Wang; Jinglai Li
Journal:  Neural Comput       Date:  2018-09-14       Impact factor: 2.026

6.  Constraining Effective Field Theories with Machine Learning.

Authors:  Johann Brehmer; Kyle Cranmer; Gilles Louppe; Juan Pavez
Journal:  Phys Rev Lett       Date:  2018-09-14       Impact factor: 9.161

7.  Mining gold from implicit models to improve likelihood-free inference.

Authors:  Johann Brehmer; Gilles Louppe; Juan Pavez; Kyle Cranmer
Journal:  Proc Natl Acad Sci U S A       Date:  2020-02-20       Impact factor: 11.205

8.  Likelihood-free inference via classification.

Authors:  Michael U Gutmann; Ritabrata Dutta; Samuel Kaski; Jukka Corander
Journal:  Stat Comput       Date:  2017-03-13       Impact factor: 2.559

  8 in total
  32 in total

1.  The science of deep learning.

Authors:  Richard Baraniuk; David Donoho; Matan Gavish
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-23       Impact factor: 11.205

2.  Training deep neural density estimators to identify mechanistic models of neural dynamics.

Authors:  Pedro J Gonçalves; Jan-Matthis Lueckmann; Michael Deistler; Marcel Nonnenmacher; Kaan Öcal; Giacomo Bassetto; Chaitanya Chintaluri; William F Podlaski; Sara A Haddad; Tim P Vogels; David S Greenberg; Jakob H Macke
Journal:  Elife       Date:  2020-09-17       Impact factor: 8.140

3.  Stimulating at the right time to recover network states in a model of the cortico-basal ganglia-thalamic circuit.

Authors:  Timothy O West; Peter J Magill; Andrew Sharott; Vladimir Litvak; Simon F Farmer; Hayriye Cagnan
Journal:  PLoS Comput Biol       Date:  2022-03-04       Impact factor: 4.475

4.  Hierarchical Bayesian models of transcriptional and translational regulation processes with delays.

Authors:  Mark Jayson Cortez; Hyukpyo Hong; Boseung Choi; Jae Kyoung Kim; Krešimir Josić
Journal:  Bioinformatics       Date:  2021-08-27       Impact factor: 6.931

5.  Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.

Authors:  Grace Avecilla; Julie N Chuong; Fangfei Li; Gavin Sherlock; David Gresham; Yoav Ram
Journal:  PLoS Biol       Date:  2022-05-27       Impact factor: 9.593

6.  Recommendations for improving statistical inference in population genomics.

Authors:  Parul Johri; Charles F Aquadro; Mark Beaumont; Brian Charlesworth; Laurent Excoffier; Adam Eyre-Walker; Peter D Keightley; Michael Lynch; Gil McVean; Bret A Payseur; Susanne P Pfeifer; Wolfgang Stephan; Jeffrey D Jensen
Journal:  PLoS Biol       Date:  2022-05-31       Impact factor: 9.593

7.  Interrogating theoretical models of neural computation with emergent property inference.

Authors:  Sean R Bittner; Agostina Palmigiano; Alex T Piet; Chunyu A Duan; Carlos D Brody; Kenneth D Miller; John Cunningham
Journal:  Elife       Date:  2021-07-29       Impact factor: 8.140

8.  Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience.

Authors:  Alexander Fengler; Lakshmi N Govindarajan; Tony Chen; Michael J Frank
Journal:  Elife       Date:  2021-04-06       Impact factor: 8.140

9.  Machine learning for weather and climate are worlds apart.

Authors:  D Watson-Parris
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2021-02-15       Impact factor: 4.226

10.  Commentary on Robinson et al. (2021): Evaluating theories of change for public health policies using computer model discovery methods.

Authors:  Robin C Purshouse; Charlotte Buckley; Alan Brennan; John Holmes
Journal:  Addiction       Date:  2021-06-28       Impact factor: 7.256

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