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BCM: toolkit for Bayesian analysis of Computational Models using samplers.

Bram Thijssen1, Tjeerd M H Dijkstra2,3, Tom Heskes4

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Summary
This summary is machine-generated.

Computational models in biology often have uncertainty. A new toolkit, BCM, offers efficient Bayesian statistical analysis using eleven sampling algorithms, improving computational efficiency for complex biological models.

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Bayesian statisticsMarkov chain Monte CarloNested samplingSamplingSequential Monte Carlo

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Biological computational models frequently exhibit significant uncertainty.
  • Bayesian statistics can analyze this uncertainty, but sampling algorithms are computationally intensive.
  • Selecting the optimal sampling algorithm for a specific model is often unclear beforehand.

Purpose of the Study:

  • To introduce BCM, a toolkit for Bayesian analysis of computational models using samplers.
  • To provide efficient implementations of eleven sampling algorithms for posterior distributions and marginal likelihoods.
  • To offer tools for simplified model specification and results visualization.

Main Methods:

  • Development of the BCM toolkit with efficient, multithreaded implementations of eleven sampling algorithms.
  • Integration of tools for model specification and results visualization.
  • Application of BCM to a cell cycle model using ordinary differential equations for an inference task.

Main Results:

  • BCM provides efficient implementations of eleven sampling algorithms for Bayesian inference.
  • The toolkit includes features for streamlined model setup and result visualization.
  • BCM demonstrated significantly greater efficiency than existing software in an example cell cycle model inference, enabling more complex problems.

Conclusions:

  • BCM serves as a comprehensive and efficient solution for computational modelers.
  • The toolkit facilitates the application of sampler-based Bayesian statistics to biological models.
  • BCM enhances the ability to tackle challenging inference problems in computational biology.