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Journal articles on the topic 'Approximate Bayesian Computation'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Beaumont, Mark A. "Approximate Bayesian Computation." Annual Review of Statistics and Its Application 6, no. 1 (2019): 379–403. http://dx.doi.org/10.1146/annurev-statistics-030718-105212.

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Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models. This review gives an overview of the method and the main issues and challenges that are the subject of current research.

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2

Sunnåker, Mikael, Alberto Giovanni Busetto, Elina Numminen, Jukka Corander, Matthieu Foll, and Christophe Dessimoz. "Approximate Bayesian Computation." PLoS Computational Biology 9, no. 1 (2013): e1002803. http://dx.doi.org/10.1371/journal.pcbi.1002803.

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3

Celeux, Gilles. "Approximate Bayesian computation methods." Statistics and Computing 22, no. 6 (2012): 1165–66. http://dx.doi.org/10.1007/s11222-012-9350-8.

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4

Beaumont, M. A., J. M. Cornuet, J. M. Marin, and C. P. Robert. "Adaptive approximate Bayesian computation." Biometrika 96, no. 4 (2009): 983–90. http://dx.doi.org/10.1093/biomet/asp052.

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5

Templeton, Alan R. "Correcting Approximate Bayesian Computation." Trends in Ecology & Evolution 25, no. 9 (2010): 488–89. http://dx.doi.org/10.1016/j.tree.2010.06.009.

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6

Prescott, Thomas P., and Ruth E. Baker. "Multifidelity Approximate Bayesian Computation." SIAM/ASA Journal on Uncertainty Quantification 8, no. 1 (2020): 114–38. http://dx.doi.org/10.1137/18m1229742.

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7

Turner, Brandon M., and Trisha Van Zandt. "Hierarchical Approximate Bayesian Computation." Psychometrika 79, no. 2 (2013): 185–209. http://dx.doi.org/10.1007/s11336-013-9381-x.

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8

Fearnhead, Paul, and Dennis Prangle. "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74, no. 3 (2012): 419–74. http://dx.doi.org/10.1111/j.1467-9868.2011.01010.x.

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9

Martin, James S., Ajay Jasra, Sumeetpal S. Singh, Nick Whiteley, Pierre Del Moral, and Emma McCoy. "Approximate Bayesian Computation for Smoothing." Stochastic Analysis and Applications 32, no. 3 (2014): 397–420. http://dx.doi.org/10.1080/07362994.2013.879262.

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10

Franks, Jordan J. "Handbook of Approximate Bayesian Computation." Journal of the American Statistical Association 115, no. 532 (2020): 2100–2101. http://dx.doi.org/10.1080/01621459.2020.1846973.

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11

Jasra, Ajay, Sumeetpal S. Singh, James S. Martin, and Emma McCoy. "Filtering via approximate Bayesian computation." Statistics and Computing 22, no. 6 (2010): 1223–37. http://dx.doi.org/10.1007/s11222-010-9185-0.

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12

Beaumont, Mark A., Wenyang Zhang, and David J. Balding. "Approximate Bayesian Computation in Population Genetics." Genetics 162, no. 4 (2002): 2025–35. http://dx.doi.org/10.1093/genetics/162.4.2025.

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Abstract We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors. Properties of the posterior distribution of a parameter, such as its mean or density curve, are approximated without explicit likelihood calculations. This is achieved by fitting a local-linear regression of simulated parameter values on simulated summary statistics, and then substituting the observed summary statistics into the regression equa

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13

Auzina, Ilze A., and Jakub M. Tomczak. "Approximate Bayesian Computation for Discrete Spaces." Entropy 23, no. 3 (2021): 312. http://dx.doi.org/10.3390/e23030312.

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Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by re-defining the standard ABC parameters to discrete ones and by introducing a novel Markov kernel that is i

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14

Drovandi, Christopher C., Anthony N. Pettitt, and Malcolm J. Faddy. "Approximate Bayesian computation using indirect inference." Journal of the Royal Statistical Society: Series C (Applied Statistics) 60, no. 3 (2011): 317–37. http://dx.doi.org/10.1111/j.1467-9876.2010.00747.x.

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15

Blum, Michael G. B. "Approximate Bayesian Computation: A Nonparametric Perspective." Journal of the American Statistical Association 105, no. 491 (2010): 1178–87. http://dx.doi.org/10.1198/jasa.2010.tm09448.

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16

Baragatti, Meïli, and Pierre Pudlo. "An overview on Approximate Bayesian computation." ESAIM: Proceedings 44 (January 2014): 291–99. http://dx.doi.org/10.1051/proc/201444018.

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17

Chiachio, Manuel, James L. Beck, Juan Chiachio, and Guillermo Rus. "Approximate Bayesian Computation by Subset Simulation." SIAM Journal on Scientific Computing 36, no. 3 (2014): A1339—A1358. http://dx.doi.org/10.1137/130932831.

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18

Turner, Brandon M., and Trisha Van Zandt. "A tutorial on approximate Bayesian computation." Journal of Mathematical Psychology 56, no. 2 (2012): 69–85. http://dx.doi.org/10.1016/j.jmp.2012.02.005.

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19

Turner, Brandon M., and Per B. Sederberg. "Approximate Bayesian computation with differential evolution." Journal of Mathematical Psychology 56, no. 5 (2012): 375–85. http://dx.doi.org/10.1016/j.jmp.2012.06.004.

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20

Frazier, D. T., G. M. Martin, C. P. Robert, and J. Rousseau. "Asymptotic properties of approximate Bayesian computation." Biometrika 105, no. 3 (2018): 593–607. http://dx.doi.org/10.1093/biomet/asy027.

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21

Csilléry, Katalin, Michael G. B. Blum, Oscar E. Gaggiotti, and Olivier François. "Approximate Bayesian Computation (ABC) in practice." Trends in Ecology & Evolution 25, no. 7 (2010): 410–18. http://dx.doi.org/10.1016/j.tree.2010.04.001.

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22

Spiliopoulos, Konstantinos. "Information Geometry for Approximate Bayesian Computation." SIAM/ASA Journal on Uncertainty Quantification 8, no. 1 (2020): 229–60. http://dx.doi.org/10.1137/18m123284x.

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23

Biau, Gérard, Frédéric Cérou, and Arnaud Guyader. "New insights into Approximate Bayesian Computation." Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 51, no. 1 (2015): 376–403. http://dx.doi.org/10.1214/13-aihp590.

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24

Park, Jinhee, and Junseok Kwon. "Wasserstein approximate bayesian computation for visual tracking." Pattern Recognition 131 (November 2022): 108905. http://dx.doi.org/10.1016/j.patcog.2022.108905.

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25

Park, Mijung, Margarita Vinaroz, and Wittawat Jitkrittum. "ABCDP: Approximate Bayesian Computation with Differential Privacy." Entropy 23, no. 8 (2021): 961. http://dx.doi.org/10.3390/e23080961.

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We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met. If the condition is sparsely met during the repeated queries, SVT can drastically reduce the cumulative privacy loss, unlike the usual case where every query incurs the privacy loss. In ABC, the quantity

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26

Nguyen, Hien Duy, Julyan Arbel, Hongliang Lu, and Florence Forbes. "Approximate Bayesian Computation Via the Energy Statistic." IEEE Access 8 (2020): 131683–98. http://dx.doi.org/10.1109/access.2020.3009878.

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27

Calvet, Laurent E., and Veronika Czellar. "Accurate Methods for Approximate Bayesian Computation Filtering." Journal of Financial Econometrics 13, no. 4 (2014): 798–838. http://dx.doi.org/10.1093/jjfinec/nbu019.

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28

Jasra, Ajay, Seongil Jo, David Nott, Christine Shoemaker, and Raul Tempone. "Multilevel Monte Carlo in approximate Bayesian computation." Stochastic Analysis and Applications 37, no. 3 (2019): 346–60. http://dx.doi.org/10.1080/07362994.2019.1566006.

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29

Bernton, Espen, Pierre E. Jacob, Mathieu Gerber, and Christian P. Robert. "Approximate Bayesian computation with the Wasserstein distance." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81, no. 2 (2019): 235–69. http://dx.doi.org/10.1111/rssb.12312.

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30

Baudet, C., B. Donati, B. Sinaimeri, et al. "Cophylogeny Reconstruction via an Approximate Bayesian Computation." Systematic Biology 64, no. 3 (2014): 416–31. http://dx.doi.org/10.1093/sysbio/syu129.

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31

Fan, Helen Hang, and Laura S. Kubatko. "Estimating species trees using approximate Bayesian computation." Molecular Phylogenetics and Evolution 59, no. 2 (2011): 354–63. http://dx.doi.org/10.1016/j.ympev.2011.02.019.

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32

Ruli, Erlis, Nicola Sartori, and Laura Ventura. "Approximate Bayesian computation with composite score functions." Statistics and Computing 26, no. 3 (2015): 679–92. http://dx.doi.org/10.1007/s11222-015-9551-z.

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33

Warne, David J., Ruth E. Baker, and Matthew J. Simpson. "Multilevel rejection sampling for approximate Bayesian computation." Computational Statistics & Data Analysis 124 (August 2018): 71–86. http://dx.doi.org/10.1016/j.csda.2018.02.009.

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34

Beaumont, Mark A. "Approximate Bayesian Computation in Evolution and Ecology." Annual Review of Ecology, Evolution, and Systematics 41, no. 1 (2010): 379–406. http://dx.doi.org/10.1146/annurev-ecolsys-102209-144621.

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35

Li, Wentao, and Paul Fearnhead. "Convergence of regression-adjusted approximate Bayesian computation." Biometrika 105, no. 2 (2018): 301–18. http://dx.doi.org/10.1093/biomet/asx081.

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36

Minter, Amanda, and Renata Retkute. "Approximate Bayesian Computation for infectious disease modelling." Epidemics 29 (December 2019): 100368. http://dx.doi.org/10.1016/j.epidem.2019.100368.

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37

Andrade, P., and L. Rifo. "Long-range dependence and approximate Bayesian computation." Communications in Statistics - Simulation and Computation 46, no. 2 (2016): 1219–37. http://dx.doi.org/10.1080/03610918.2014.995816.

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38

Lenormand, Maxime, Franck Jabot, and Guillaume Deffuant. "Adaptive approximate Bayesian computation for complex models." Computational Statistics 28, no. 6 (2013): 2777–96. http://dx.doi.org/10.1007/s00180-013-0428-3.

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39

Fan, Yanan, David J. Nott, and Scott A. Sisson. "Approximate Bayesian computation via regression density estimation." Stat 2, no. 1 (2013): 34–48. http://dx.doi.org/10.1002/sta4.15.

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40

Zuluaga, Carlos David, and Mauricio A. Alvarez. "Bayesian Probabilistic Power Flow Analysis Using Jacobian Approximate Bayesian Computation." IEEE Transactions on Power Systems 33, no. 5 (2018): 5217–25. http://dx.doi.org/10.1109/tpwrs.2018.2810641.

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41

Buzbas, Erkan O., and Noah A. Rosenberg. "AABC: Approximate approximate Bayesian computation for inference in population-genetic models." Theoretical Population Biology 99 (February 2015): 31–42. http://dx.doi.org/10.1016/j.tpb.2014.09.002.

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42

Zhou, Jin, and Kenji Fukumizu. "Local Kernel Dimension Reduction in Approximate Bayesian Computation." Open Journal of Statistics 08, no. 03 (2018): 479–96. http://dx.doi.org/10.4236/ojs.2018.83031.

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43

Bartoszek, K., and P. Liò. "Modelling Trait-dependent Speciation with Approximate Bayesian Computation." Acta Physica Polonica B Proceedings Supplement 12, no. 1 (2019): 25. http://dx.doi.org/10.5506/aphyspolbsupp.12.25.

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44

Akeret, Joël, Alexandre Refregier, Adam Amara, Sebastian Seehars, and Caspar Hasner. "Approximate Bayesian computation for forward modeling in cosmology." Journal of Cosmology and Astroparticle Physics 2015, no. 08 (2015): 043. http://dx.doi.org/10.1088/1475-7516/2015/08/043.

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45

Navascués, Miguel, Raphaël Leblois, and Concetta Burgarella. "Demographic inference through approximate-Bayesian-computation skyline plots." PeerJ 5 (July 18, 2017): e3530. http://dx.doi.org/10.7717/peerj.3530.

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The skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. He

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46

Sels, Dries, Hesam Dashti, Samia Mora, Olga Demler, and Eugene Demler. "Quantum approximate Bayesian computation for NMR model inference." Nature Machine Intelligence 2, no. 7 (2020): 396–402. http://dx.doi.org/10.1038/s42256-020-0198-x.

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47

Hickerson, Michael J., Eli A. Stahl, and H. A. Lessios. "TEST FOR SIMULTANEOUS DIVERGENCE USING APPROXIMATE BAYESIAN COMPUTATION." Evolution 60, no. 12 (2006): 2435–53. http://dx.doi.org/10.1111/j.0014-3820.2006.tb01880.x.

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48

Karabatsos, George. "Copula Approximate Bayesian Computation Using Distribution Random Forests." Stats 7, no. 3 (2024): 1002–50. http://dx.doi.org/10.3390/stats7030061.

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Ongoing modern computational advancements continue to make it easier to collect increasingly large and complex datasets, which can often only be realistically analyzed using models defined by intractable likelihood functions. This Stats invited feature article introduces and provides an extensive simulation study of a new approximate Bayesian computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihoods, that unifies and extends previous ABC methods proposed separately. This framewo

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49

Picchini, Umberto. "Inference for SDE Models via Approximate Bayesian Computation." Journal of Computational and Graphical Statistics 23, no. 4 (2014): 1080–100. http://dx.doi.org/10.1080/10618600.2013.866048.

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50

Buchholz, Alexander, and Nicolas Chopin. "Improving Approximate Bayesian Computation via Quasi-Monte Carlo." Journal of Computational and Graphical Statistics 28, no. 1 (2018): 205–19. http://dx.doi.org/10.1080/10618600.2018.1497511.

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