ArticleBMC systems biology2016
BCM: toolkit for Bayesian analysis of Computational Models using samplers.
Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation.Bioinformatics (Oxford, England) · 2020Article
- Exposing Hidden High-Affinity RNA Conformational States.Journal of the American Chemical Society · 2020Article
- Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference.PloS one · 2020Article
- Defining informative priors for ensemble modeling in systems biology.Nature protocols · 2018Article
- Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates.Bioinformatics (Oxford, England) · 2018Article
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4 authors.
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Abstract
backgroundComputational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model.
resultsWe present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved.
conclusionsBCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.
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