popodds

Name	popodds JSON
Version	0.7.1 JSON
	download
home_page	https://github.com/mdmould/popodds
Summary	Simple package for Bayesian model comparison.
upload_time	2024-07-11 03:10:40
maintainer	None
docs_url	None
author	Matthew Mould
requires_python	>=3.7
license	MIT
keywords
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # 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.

            

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