Print Email Facebook Twitter BMC Title BMC: Toolkit for Bayesian analysis of Computational Models using samplers Author Thijssen, B. (TU Delft Pattern Recognition and Bioinformatics) Dijkstra, Tjeerd M.H. (Max Planck Institute for Developmental Biolgy; University Clinic Tübingen) Heskes, Tom (Radboud Universiteit Nijmegen) Wessels, L.F.A. (TU Delft Pattern Recognition and Bioinformatics; Netherlands Cancer Institute) Date 2016 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. Subject Bayesian statisticsSamplingMarkov chain Monte CarloSequential Monte CarloNested sampling To reference this document use: http://resolver.tudelft.nl/uuid:60fa4673-b138-4b56-ac98-6e7d812e19d9 DOI https://doi.org/10.1186/s12918-016-0339-3 ISSN 1752-0509 Source BMC Systems Biology, 1-8 Part of collection Institutional Repository Document type journal article Rights © 2016 B. Thijssen, Tjeerd M.H. Dijkstra, Tom Heskes, L.F.A. Wessels Files PDF 11578161.pdf 1.6 MB Close viewer /islandora/object/uuid:60fa4673-b138-4b56-ac98-6e7d812e19d9/datastream/OBJ/view