Metadata-Version: 2.1
Name: bayesml
Version: 0.1.1
Summary: A library for Bayes statistics, Bayes decision theory, and Bayes machine learning
Home-page: https://yuta-nakahara.github.io/BayesML/
Author: Yuta Nakahara et al.
Author-email: yuta.nakahara@aoni.waseda.jp
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Requires-Python: ~=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt

## Purpose

BayesML is a library designed for promoting research, education, 
and application of machine learning based on Bayesian statistics 
and Bayesian decision theory. 
Through these activities, BayesML aims to contribute to society.

## Characteristics

BayesML has the following characteristics.

* The structure of the library reflects the philosophy of 
  Bayesian statistics and Bayesian decision theory: 
  updating the posterior distribution learned from the data 
  and outputting the optimal estimate based on the Bayes criterion.
* Many of our learning algorithms are much faster than general-purpose 
  Bayesian learning algorithms such as MCMC methods because they 
  effectively use the conjugate property of a probabilistic data 
  generative model and a prior distribution. 
  Moreover, they are suitable for online learning.
* All packages have methods to visualize the probabilistic data 
  generative model, generated data from that model, and the posterior 
  distribution learned from the data in 2~3 dimensional space. 
  Thus, you can effectively understand the characteristics of 
  probabilistic data generative models and algorithms through 
  the generation of synthetic data and learning from them.


