Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

The Nature of Statistical Learning Theory

We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

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Latent Dirichlet Allocation

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

The application of maximum likelihood techniques to the estimation of evolutionary trees from nucleic acid sequence data is discussed. A computationally feasible method for finding such maximum likelihood estimates is developed, and a computer program is available. This method has advantages over the traditional parsimony algorithms, which can give misleading results if rates of evolution differ in different lineages. It also allows the testing of hypotheses about the constancy of evolutionary rates by likelihood ratio tests, and gives rough indication of the error of the estimate of the tree.

Evolutionary trees from DNA sequences: A maximum likelihood approach

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

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Chapter 3: The Likelihood Principle and Generalizations

The problem of testing mutually exclusive hypotheses with dependent test statistics is considered. Bayesian and frequentist approaches to multiplicity control are studied and compared to help gain understanding as to the effect of test statistic dependence on each approach. The Bayesian approach is shown to have excellent frequentist properties and is argued to be the most effective way of obtaining frequentist multiplicity control, without sacrificing power, when there is considerable test statistic dependence.

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Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence

Bayesian Variable Selection in Linear Regression: Comment

Several problems in Bayesian statistics that involve significant stochastic computation are explored. The considered problems either involve model selection or statistical inverse problems. 
The first three chapters of the thesis consider model selection. The first problem analyzed is model selection in multiple linear regression. Several approaches are compared and a new algorithm for searching in large model spaces is introduced. The second problem considered is choice among probit models with binary covariates; Again, the key to successful computation is the introduction of a fast search algorithm. The third problem considered is comparison of Expected Posterior Priors with other commonly used techniques in model selection. 
The second part of the thesis introduces an example of the construction of fast simulators for assessment and propagation of input uncertainty when using complex computer models. This is studied in the context of microsimulation of traffic systems. 
The last chapter of the thesis introduces Bayesian methods for analysis of multiscale stochastic volatility models and tests its performance on foreign exchange data. This chapter involves what could be termed an informal approach to selection between stochastic volatility models with different number of scales.

James O. Berger

Papers

Chapter 3: The Likelihood Principle and Generalizations

Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence

Bayesian Variable Selection in Linear Regression: Comment

Bayesian stochastic computation with application to model selection and inverse problems

Bayesian analysis with limited communication