BCM

BCM performs Bayesian inference and uncertainty quantification for computational models used in biological research.

Key Features:

• Sampling algorithms: Implements eleven distinct sampling algorithms for exploring posterior probability distributions.
• Marginal likelihood estimation: Calculates marginal likelihoods to support model comparison.
• Multithreaded execution: Executes sampling algorithms using multithreaded processes to accelerate computation.
• Model specification tools: Provides tools for specifying computational models.
• Result visualization scripts: Includes scripts for visualization of inference results.
• Architecture: Employs a flexible architecture to support a range of biological computational models.
• Demonstrated performance: Shows reported efficiency improvements over other software on inference tasks such as a cell-cycle model based on ordinary differential equations.

Scientific Applications:

• Bayesian inference for biological models: Sampling posterior probability distributions of parameters in computational biological models.
• Uncertainty quantification: Quantifying and managing uncertainty in model parameters and predictions.
• Model comparison: Estimating marginal likelihoods to compare competing models.
• Inference for ODE-based systems: Performing sampler-based inference on ordinary differential equation models such as cell-cycle models.

Methodology:

Uses eleven distinct sampling algorithms to sample posterior probability distributions and calculate marginal likelihoods, executed with multithreaded processes; includes model specification tools and result visualization scripts.