In analyzing my data I wanted to classify it with a naive Bayesian classifier. I wasn't sure I had the math right, so I wrote a tiny abstract classifier to test with. The code is pretty cool:
This project contains Naive and Fishers bayesian classifiers, as described in Toby Segaran's book "Programming Collective Intelligence." The book has python implementations; this is a Java implementation.
sux0r 2.0 is an extendable content management system (CMS) built around the principles of Naive Bayesian probabilistic content.
Naive Bayesian Categorization is the ouija board of mathematics. Known for being good at filtering junk mail, the Naive Bayesian algorithm can categorize anything so long as there are coherent reference texts to work from. For example, categorizing documents in relation to a vector of political manifestos, or religious holy books, make for a neat trick. More subjective magic 8-ball categories could be "good vs. bad" or company press releases in relation to stock market prices.
In addition to being a blog, RSS aggregator, bookmark repository, and photo publishing platform, sux0r 2.0 allows users to maintain multiple lists of Naive Bayesian categories. These category lists, called vectors, can be shared with other users. This allows a group of trusted friends to share, train, and use sux0r together.
An Intuitive Explanation of Bayesian Reasoning Bayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction. By Eliezer Yudkowsky Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule",
A. Souza, L. Nardi, L. Oliveira, K. Olukotun, M. Lindauer, und F. Hutter. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), (September 2021)To appear.
S. Sriraman, L. Kevrekidis, und G. Hummer. JOURNAL OF PHYSICAL CHEMISTRY B, 109 (14):
6479-6484(April 2005)Coarse Master Equation from Bayesian Analysis of Replica Molecular Dynamics Simulations
Saravanapriyan Sriraman, Ioannis G. Kevrekidis, and Gerhard Hummer
pp 6479–6484
Publication Date (Web): January 25, 2005 (Article)
DOI: 10.1021/jp046448u.