Gaussian Processes for Machine Learning: Book webpage. Carl Edward Rasmussen and Christopher K. I. Williams The MIT Press, 2006. ISBN 0-262-18253-X. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms.
The book is available for download in electronic format. The book was awarded the 2009 DeGroot Prize of the International Society for Bayesian Analysis. How Not To Sort By Average Rating. By Evan Miller February 6, 2009 (Changes) Translations: Russian Ukrainian Estonian PROBLEM: You are a web programmer. You have users. WRONG SOLUTION #1: Score = (Positive ratings) − (Negative ratings) Why it is wrong: Suppose one item has 600 positive ratings and 400 negative ratings: 60% positive.
Sites that make this mistake: Urban Dictionary WRONG SOLUTION #2: Score = Average rating = (Positive ratings) / (Total ratings) Why it is wrong: Average rating works fine if you always have a ton of ratings, but suppose item 1 has 2 positive ratings and 0 negative ratings. Sites that make this mistake: Amazon.com CORRECT SOLUTION: Score = Lower bound of Wilson score confidence interval for a Bernoulli parameter Say what: We need to balance the proportion of positive ratings with the uncertainty of a small number of observations.
(Use minus where it says plus/minus to calculate the lower bound.) You will quickly see that the extra bit of math makes all the good stuff bubble up to the top. Wilson, E. Learning R - Part I. The top ten things that math probability says about the real wor. By David Aldous. Talk given at Cornell University, April 14 2008. Like most scientists, I habitually just write slides and extemporize the actual spoken words. For this one talk I wrote the following script, though (being hard to break the habits of 30 years) I didn't stick closely to the script. Each link is one slide. click here to read Belorussian translation. Every academic discipline has its own peculiarities, and let me start by pointing out a peculiarity that my own topic (math probability) inherits from its parent mathematics.
Why is this peculiar? So here is my manifesto. At least, here are 6 of them. Opinion polls, like airplanes, actually work rather well -- they only get into the news when things go wrong. Separating skill from luck in the aggregate is one conceptual aspect of the regression effect. The fact that most letter strings JQTOMXDW KKYSC have no meaning is what makes most simple letter substitution ciphers easy to break. The final item is more frivolous. PROBABILITY THEORY -- THE LOGIC OF SCIENCE. By E. T. Jaynes Wayman Crow Professor of Physics Washington University St. Louis, MO 63130, U.S.A. Dedicated to the Memory of Sir Harold Jeffreys, who saw the truth and preserved it. Fragmentary Edition of June 1994 First three chapters in DJVU Short Contents General comments (BY OTHERS, NOT E.T.
Chapter 19 Physical MeasurementsChapter 20 Regression and Linear ModelsChapter 21 Estimation with Cauchy and t--DistributionsChapter 22 Time Series Analysis and Autoregressive ModelsChapter 23 Spectrum / Shape AnalysisChapter 24 Model Comparison and RobustnessChapter 25 Image ReconstructionChapter 26 Marginalization TheoryChapter 27 Communication TheoryChapter 28 Optimal Antenna and Filter DesignChapter 29 Statistical MechanicsChapter 30 Conclusions List of references First 95 pages (Adobe's pdf format) at once from bayes.wustl.edu.