popodds 0.6.2

Simple package for Bayesian model comparison.

popodds

Simple package for Bayesian model comparison.

Given samples from a posterior distribution inferred under some default prior, compute the Bayes factor or odds in favour of a new prior model.

Installation

pip install popodds

Usage

The package consists of the ModelComparison class to compute Bayes factors, and a wrapper function log_odds for simplicity.

The computation only requires a few ingredients:

• model a new prior model or samples from it,
• prior the original parameter estimation prior or samples from it
• samples samples from a parameter estimation run.

Optional:

• model_bounds parameter bounds for the new prior model,
• prior_bounds parameter bounds for the original prior model,
• log compute probability densities in log space,
• prior_odds odds between the prior models, which defaults to unity,
• second_model model to compute odds against instead of prior,
• second_bounds parameter bounds for the second model,
• detectable compare between detectable rather than intrinsic populations.