BayesPy
Bayesian inference tools in Python for estimating Dirichlet priors and multinomial mixture models from discrete event data.
What is BayesPy?
BayesPy is a Python library for Bayesian inference that focuses on estimating Dirichlet priors and multinomial mixture models from discrete event data. It helps users compute probability distributions from historical counts to improve predictions of future events, using gradient descent for parameter estimation.
Data scientists, statisticians, and researchers working with categorical data who need to apply Bayesian methods for probability estimation and mixture modeling.
It provides a straightforward, command-line-driven approach to Bayesian inference, simplifying complex statistical estimations like Dirichlet prior fitting and multinomial mixture analysis without requiring extensive statistical software.
Overview
Bayesian Inference Tools in Python
Use Cases
Best For
- Estimating Dirichlet priors from historical count data
- Modeling multinomial mixture distributions for categorical data
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