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Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.
Wow! PDF file of the whole book online, plus some R examples.
Networks, Crowds, and Markets: A Book by David Easley and Jon Kleinberg
"Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. Drawing on ideas from economics, sociology, computing and information science, and applied mathematics, it describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.
The book is based on an inter-disciplinary course entitled Networks that we teach at Cornell. The book, like the course, is designed at the introductory undergraduate level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with optional advanced sections. "
Rough Type: Nicholas Carr's Blog: Close down the schools!
"Finally, it should be noted that, of the 51 experiments studied, only 11 actually showed a statistically significant advantage to online instruction."
CRAN - Package HybridMC
This package is an R implementation of the Hybrid Monte Carlo and Multipoint Hybrid Monte Carlo sampling techniques described in Liu (2001): "Monte Carlo Strategies in Computing
Estimating Effects and Correlations in Neuroimaging Data
Good list of presenters: Gelman, Shalizi, the "Voodoo statistics" guy.
How to design a mini-lecture on statistical inference? - Statistical Modeling, Causal Inference, and Social Science
Teaching advice from Gelman
http://www.stanford.edu/~montanar/BOOK/book.html
It should be an introduction to a rich and rapidly evolving research field at the interface between statistical physics, theretical computer science/discrete mathematics, and coding/information theory. It should be accessible to graduate students an researchers without specific training in any of these three fields.
[physics/9701026] Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
"Gaussian processes are a natural way of defining prior distributions over functions of one or more input variables. In a simple nonparametric regression problem, where such a function gives the mean of a Gaussian distribution for an observed response, a Gaussian process model can easily be implemented using matrix computations that are feasible for datasets of up to about a thousand cases. Hyperparameters that define the covariance function of the Gaussian process can be sampled using Markov chain methods. Regression models where the noise has a t distribution and logistic or probit models for classification applications can be implemented by sampling as well for latent values underlying the observations. Software is now available that implements these methods using covariance functions with hierarchical parameterizations. Models defined in this way can discover high-level properties of the data, such as which inputs are relevant to predicting the response. "
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