development of self-healing systems capable of making inferences about their own behavior, such as diagnosing faults and performance degradations. uses a cost-efficient technique for adaptive diagnosis that combines probabilistic inference with online, active selection of the most-informative measurements called probes. Probes are end-to-end test transactions that collect information about the availability and performance of a distributed system. Given the probe results (symptoms), RAIL performs Bayesian inference in order to find the most likely explanation (cause), An important difference between RAIL's approach and ''passive'' data analysis is in RAIL's ability to select and execute probes online. This approach, called active probing, uses an information-theoretic criterion called information gain in order to select adaptively only a small set of the most informative probes at any given time; this approach significantly reduces the overall number of probes required
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.