BayesianTools: MCMC and SMC Samplers for Complex Model Calibration

BayesianTools provides general-purpose Markov Chain Monte Carlo and Sequential Monte Carlo samplers for Bayesian statistics. The package, authored by Florian Härtig, includes various Metropolis MCMC variants, the T-walk, differential evolution MCMCs, DREAM MCMCs, and an SMC particle filter. It focuses on calibrating complex system models and offers plot and diagnostic functions.

Use Cases

• Calibrating complex system models based on the described focus of the package.
• Performing Bayesian inference using the implemented MCMC and SMC samplers.
• Diagnosing and visualizing sampling results based on the included plot and diagnostic functions.
• Comparing sampling algorithm performance using the variety of implemented samplers.

Strengths

• Implements multiple established sampling algorithms, including various Metropolis MCMC variants, the T-walk, and DREAM MCMCs.
• Includes a sequential Monte Carlo particle filter sampler.
• Provides diagnostic and plotting functions for analysis.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Software package for statistical sampling.

Time Range

Temporal coverage is unknown.

Freshness

Last update date is unknown; freshness unverified.

Geography

Spatial coverage is unknown.