Abstract
BayesFit is a module for Python that allows users to fit models to psychophysical data using Bayesian inference. The module aims to make it easier to develop probabilistic models for psychophysical data in Python by providing users with a simple API that streamlines the process of defining psychophysical models, obtaining fits, extracting outputs, and visualizing fitted models. Our software implementation uses numerical integration as the primary tool to fit models, which avoids the complications that arise in using Markov Chain Monte Carlo (MCMC) methods [1]. The source code for BayesFit is available at https://github.com/slugocm/bayesfit and API documentation at http://www.slugocm.ca/bayesfit/ . This module is extensible, and many of the functions primarily rely on Numpy [2] and therefore can be reused as newer versions of Python are developed to ensure researchers always have a tool available to ease the process of fitting models to psychophysical data.
Highlights
BayesFit is a module for Python that allows users to fit models to psychophysical data using Bayesian inference
This module is extensible, and many of the functions primarily rely on Numpy [2] and can be reused as newer versions of Python are developed to ensure researchers always have a tool available to ease the process of fitting models to psychophysical data
Fitting a psychometric function to data is a common component of the analysis of behavioural data, very few tools have been designed to help researchers streamline this fitting process [1, 3, 4, 5], and only one of these has been adapted for use in Python [1]
Summary
BayesFit is a module for Python that allows users to fit models to psychophysical data using Bayesian inference. BayesFit uses only one main function called fitmodel to streamline the process of fitting models to psychophysical data.
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