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ABC-MCMC specifications


Unique identifier OMICS_10596
Alternative name Approximate Bayesian Computation Markov chain Monte Carlo estimation
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes


No version available



  • person_outline Allen G. Rodrigo

Publication for Approximate Bayesian Computation Markov chain Monte Carlo estimation

ABC-MCMC citations


Guest Editorial—Special Collection Topic: Statistical Systems Theory in Cancer Modeling, Diagnosis, and Therapy

Cancer Inform
PMCID: 5843086
PMID: 29531471
DOI: 10.1177/1176935118760944

[…] nding malignant and benign tissues, the aim being to cluster the liver regions on the basis of their computed tomography perfusion profiles, which can be used for diagnosing malignant tissue.Bayesian ABC-MCMC Classification of Liquid Chromatography-Mass Spectrometry Data by Banerjee and Braga-Neto applies the optimal Bayesian classifier using a model of the liquid chromatography-mass spectrometry […]


Inferring epidemiological parameters from phylogenies using regression ABC: A comparative study

PLoS Comput Biol
PMCID: 5358897
PMID: 28263987
DOI: 10.1371/journal.pcbi.1005416

[…] target in view of the computed distance. These constitute the final posterior distribution of the parameters. Over the last decade, several improvements of the rejection algorithm have been proposed. ABC-MCMC consists in searching in the prior parameter space more efficiently by using MCMC-like approaches []. Sequential Monte Carlo (ABC-SMC) methods adjust the posterior distribution obtained by re […]


Bayesian ABC MCMC Classification of Liquid Chromatography–Mass Spectrometry Data

Cancer Inform
PMCID: 5224349
PMID: 28096647
DOI: 10.4137/CIN.S30798

[…] n that as φ increases the expected error rates for all classification rules approach the no-information value 0.5, ie, the same error rate of flipping a coin. However, the expected error rate of the ABC-MCMC classification rule approaches 0.5 error rate rather more slowly than the others, indicating superiority in classifying noisy data. […]


Deep Learning for Population Genetic Inference

PLoS Comput Biol
PMCID: 4809617
PMID: 27018908
DOI: 10.1371/journal.pcbi.1004845

[…] ich could be incorporated into future simulations.Deep learning can make efficient use of even a limited number of simulated datasets. In this vein, it would be interesting to use an approach such as ABC MCMC [] to simulate data, and then use deep learning on these simulated datasets. Alternatively, deep learning could be used to select informative statistics for a subsequent method such as ABC [] […]


Broadwick: a framework for computational epidemiology

BMC Bioinformatics
PMCID: 4743398
PMID: 26846686
DOI: 10.1186/s12859-016-0903-2

[…] e ABC class which will run the model in the prior space until it converges and reports the calculated posteriors. The ABC class can be used in conjunction with the Markov Chain class to implement the ABC-MCMC allgorithm as proposed by []. Controllers and observers can also be supplied to the ABC class as with the Monte Carlo and Markov Chain classes.This methodology of supplying or overriding func […]


Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation

PLoS Comput Biol
PMCID: 4671693
PMID: 26642072
DOI: 10.1371/journal.pcbi.1004635

[…] %. Thus, this study suggests that it is necessary to generate 106 model simulations to obtain an ABC posterior sample of size 1,000.Several studies [–] proposed a Markov chain Monte Carlo approach to ABC (MCMC-ABC). MCMC-ABC algorithms make local proposals in high (ABC) posterior support regions, thus they can improve the acceptance rates. However, the posterior samples are highly correlated and t […]


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ABC-MCMC institution(s)
Biodesign Institute, Arizona State University, Tempe, AZ, USA; Department of Biology, Duke University, Durham, NC, USA; The National Evolutionary Synthesis Center, Durham, NC, USA

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