I apologize in advance for items missed in the reference section. These topics are pretty broad there are millions of articles covering some aspect of these topics. The most I can provide here is a subset of the articles I found useful.
Trading Links
 I like a lot of Brett’s posts which stopped in 2007 but still appear to be relevant. Blog of Brett N. Steenbarger, Ph.D. He has served as Director of Trader Development for Kingstree Trading in Chicago has been involved in trading since 1970’s. His article newer blog Herding Sentiment in the Stock Market and Prospective Index Returns and the prior entry Herding Behavior in the Stock Market: A Look at Volume Concentration nicely define herding behavior and use it as a way to detect a general up trends, down trends and how to use them to detect primary transition points.
Algorithmic Trading
 Machine Learning Techniques for Stock Prediction by Vatsal H. Shah
Machine Learning Links

Bayes / Bayesian General overview
They Bayes topic can be divided into two major categories Bayesian Inference and Bayesian classification. These are two very different approaches with very different implementation constraints but they can be applied either together or in isolation.
Naive Bayesian classification can be limited in accuracy for some domains so it has been extended with Boosted Trees and many other proprietary approaches which adjust the weight of statistical features to influence the output. The Bayes Analytic Engine includes several extended techniques for the same purpose.
The Bayes classifier output can be combined with other ML techniques to provide Prediction capability which is also used in the Bayes Analytic system.
 How Bayes’ Rule Can Make You A Better Thinker
 An Intuitive Explanation of Bayes’ Theorem – Bayes’ Theorem for the curious and bewildered an excruciatingly gentle introduction.
 The Bayes theorem, explained to an aboveaverage squirrel
 Bayesian Prediction for Investors – Bayes Theorem tells us how to combine prior knowledge with actual observations to predict the future. And as that prior knowledge changes it tells us how to update our predictions.
 Top 5 Intelligence Analysis Methods: Bayesian Analysis
 International Society for Bayesian Analysis
 Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments by Carla Currin, Toby Mitchell, Max Morris, Don Ylvisaker.
 Probabilistic and Bayesian Analytics – Andrew W. Moore Professor School of Computer Science Carnegie Mellon University
 Bayesian Data Analysis, Second Edition Andrew Gelman (Author), John B. Carlin (Author), Hal S. Stern (Author), Donald B. Rubin (Author)
 Universality of Bayesian Predictions Alessio Sancetta
 A Bayesian Prediction Model for the U.S. Presidential Election
 How to Predict with Bayes and MDL Slides by Marcus Hutter
Machine Learning Overview
 http://sourcesandmethods.blogspot.com/2008/12/top5intelligenceanalysismethods_08.html
 http://www.reddit.com/r/Python/comments/1l1ge6/machine_learning_and_analytics/
 http://refcardz.dzone.com/refcardz/machinelearningpredictive
Machine Prediction / Predictive Analytics
Javascript Machine Learning
 http://burakkanber.com/blog/machinelearninggeneticalgorithmspart1javascript/
 http://burakkanber.com/blog/machinelearninginjsknearestneighborpart1/
 http://burakkanber.com/blog/machinelearninggeneticalgorithmsinjavascriptpart2/
 JavaScript Machine Learning and Neural Networks with Encog
F# Machine Learning

 I do not agree with several of his implementation decisions but I think his work is a great start and well worth looking at for anybody considering the use of F# for machine learning. Converting the code from “Machine Learning in Action” from Python to F#
One Reply to “Reference”