SYSBIONS: nested sampling for systems biology. Motivation: Model selection is a fundamental part of the scientific process in systems biology. Given a set of competing hypotheses, we routinely wish to choose the one that best explains the observed data. In the Bayesian framework, models are compared via Bayes factors (the ratio of evidences), where a model’s evidence is the support given to the model by the data. A parallel interest is inferring the distribution of the parameters that define a model. Nested sampling is a method for the computation of a model’s evidence and the generation of samples from the posterior parameter distribution. Results: We present a C-based, GPU-accelerated implementation of nested sampling that is designed for biological applications. The algorithm follows a standard routine with optional extensions and additional features. We provide a number of methods for sampling from the prior subject to a likelihood constraint. Availability and implementation: The software SYSBIONS is available from http://www.theosysbio.bio.ic.ac.uk/resources/sysbions/
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH
- Ke, Yuqin; Tian, Tianhai: Approximate Bayesian computational methods for the inference of unknown parameters (2019)
- Lambert, Ben; MacLean, Adam L.; Fletcher, Alexander G.; Combes, Alexander N.; Little, Melissa H.; Byrne, Helen M.: Bayesian inference of agent-based models: a tool for studying kidney branching morphogenesis (2018)