Fundamentals and Recent Developments in Approximate Bayesian Computation

Lintusaari, Jarno; Gutmann, Michael U.; Dutta, Ratna; Kaski, Samuel; Corander, Jukka

doi:10.1093/sysbio/syw077

Cited by 196 publications

(256 citation statements)

References 51 publications

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“…How to actually measure the discrepancy between the observed and the simulated data is a major difficulty in these methods (Lintusaari et al 2017;Marin et al 2012). We here show that J n can be used as a discrepancy measure in ABC; in the following, we call this approach "classifier ABC."…”

Section: Bayesian Inference Via Classificationmentioning

confidence: 94%

“…ABC comprises several simulation-based methods to obtain samples from the posterior distribution when the likelihood function is not known (for review papers, see, e.g., Lintusaari et al 2017;Marin et al 2012). ABC algorithms are iterative: The basic steps at each iteration are as follows:…”

Section: Bayesian Inference Via Classificationmentioning

confidence: 99%

“…Both the distance function used and the summary statistics are critical for the success of the inference procedure (see, for example, the reviews by Lintusaari et al (2017) and Marin et al (2012). Traditionally, researchers choose the two quantities subjectively, relying on expert knowledge about the observed data.…”

Section: Introductionmentioning

confidence: 99%

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Likelihood-free inference via classification

et al. 2017

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Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major difficulty in this framework is how to measure the discrepancy between the simulated and observed data. Transforming the original problem into a problem of classifying the data into simulated versus observed, we find that classification accuracy can be used to assess the discrepancy. The complete arsenal of classification methods becomes thereby available for inference of intractable generative models. We validate our approach using theory and simulations for both point estimation and Bayesian inference, and demonstrate its use on real data by inferring an individual-based epidemiological model for bacterial infections in child care centers.

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Section: Bayesian Inference Via Classificationmentioning

confidence: 94%

Section: Bayesian Inference Via Classificationmentioning

confidence: 99%

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Likelihood-free inference via classification

et al. 2017

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“…ABC has been used in various other fields as well, especially for inference with complex simulator models (Csilléry, Blum, Gaggiotti, & François, ). A recent review of various ABC methods was made by Lintusaari, Gutmann, Dutta, Kaski, and Corander ().…”

Section: Probabilistic Inference For Computational Cognitive Modelsmentioning

confidence: 99%

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

et al. 2019

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This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.

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“…Then, for given initial conditions and specific values of parameters it is possible to generate data, that is, provide a solution of the set of differential equations. Later, the ABC techniques were widely used in other applications, such as, biology [20,21], evolution and ecology [15,22], the evolution of genomes [23], the dynamics of gene regulation [18], the demographic spread of species [24][25][26][27], or mRNA self-regulation [28]. ABC techniques offer an almost automated solution in situations where evaluation of the posterior is computationally prohibitive, or whenever suitable likelihoods are not available.…”

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confidence: 99%

Estimating kinetic constants in the Michaelis–Menten model from one enzymatic assay using Approximate Bayesian Computation

Tomczak

Węglarz-Tomczak

2019

FEBS Letters

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The Michaelis–Menten equation is one of the most extensively used models in biochemistry for studying enzyme kinetics. However, this model requires at least a couple (e.g., eight or more) of measurements at different substrate concentrations to determine kinetic parameters. Here, we report the discovery of a novel tool for calculating kinetic constants in the Michaelis–Menten equation from only a single enzymatic assay. As a consequence, our method leads to reduced costs and time, primarily by lowering the amount of enzymes, since their isolation, storage and usage can be challenging when conducting research.

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Fundamentals and Recent Developments in Approximate Bayesian Computation

Cited by 196 publications

References 51 publications

Likelihood-free inference via classification

Likelihood-free inference via classification

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

Estimating kinetic constants in the Michaelis–Menten model from one enzymatic assay using Approximate Bayesian Computation

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