Skip to main content
Warning: You are using the test version of PyPI. This is a pre-production deployment of Warehouse. Changes made here affect the production instance of TestPyPI (testpypi.python.org).
Help us improve Python packaging - Donate today!

Bayesian data analysis library

Project Description
KCBO
====

A Bayesian testing framework written in Python.

KCBO Philosophy
---------------

*The goal of KCBO is to provide an easy to use, Bayesian framework to the masses.*

The Bayesian philosophy and framework provide an excellent structure for both asking and answering questions. Bayesian statistics allow us to ask questions in a more natural manner and derive incredibly powerful solutions.

Researchers and analysts shouldn't spend hours reading academic papers and finding which conjugate priors they need, which type of sampler their MCMC should have, or when to use MC or MCMC. Software should take care of that computing and researchers should take care of producing insights.

The world is ready for a good, clean, and easy to use Bayesian framework. The goal of KCBO is to provide that framework.
Release History

Release History

This version
History Node

0.0.1

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting