BayesFit: A tool for modeling psychophysical data using Bayesian inference

• Main Authors: Michael Slugocki, Allison B. Sekuler, Patrick Bennett
• Format: Article
• Language: English
• Published: Ubiquity Press 2019-01-01
• Series: Journal of Open Research Software
• Subjects: Psychophysics; Psychometrics; Psychometric function; Bayesian inference; Numerical integration; Curve fitting; Python
• Online Access: https://openresearchsoftware.metajnl.com/articles/202

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.