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

The problem of investigating compatibility of an assumed model with the data is investigated in the situation when the assumed model has unknown parameters. The most frequently used measures of compatibility are p values, based on statistics T for which large values are deemed to indicate incompatibility of the data and the model. When the null model has unknown parameters, p values are not uniquely defined. The proposals for computing a p value in such a situation include the plug-in and similar p values on the frequentist side, and the predictive and posterior predictive p values on the Bayesian side. We propose two alternatives, the conditional predictive p value and the partial posterior predictive p value, and indicate their advantages from both Bayesian and frequentist perspectives.

P Values for Composite Null Models

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Objective Bayesian Methods for Model Selection: Introduction and Comparison

In many scientific journals, statistical analysis is used to give the seal of objectivity to conclusions. Yet this general perception of the objectivity of statistics, and perhaps of science in general, may be misguided. Let us be careful here; objectivity is a loaded word, and the next worst thing to being a fraud is to be "nonobjective." We are not going to discuss the manner in which a scientist strives to obtain objective evidence. Rather, we will discuss whether or not it is possible to arrive at an objective conclusion based on data from an experiment. We grant that objective data can be _ obtained, but we will argue that reaching sensible conclusions from statistical analysis of these data may require subjective input. This conclusion is in no way harmful or demeaning to statistical analysis. Far from it; to acknowledge the subjectivity inherent in the inter pretation of data is to recognize the central role of statistical analysis as a formal mechanism by which new ev idence can be integrated with exist ing knowledge. Such a view of statistics as a dynamic discipline is far from the common perception of a rather dry, automatic technology for processing data. Acknowledging the subjectivity of statistical analy sis would be healthy for science as a whole for at least two reasons. The first is that the straightforward meth ods of subjective statistical analysis, called Bayesian analysis, yield answers which are much easier to under stand than standard statistical answers, and hence much less likely to be misinterpreted. This will be dramatically illustrated in our first example. The second reason is that even standard statistical

https://www.jstor.org/stable/pdfplus/27855070.pdf

James O. Berger

Papers

P Values for Composite Null Models

Objective Bayesian Methods for Model Selection: Introduction and Comparison

Statistical Analysis and the Illusion of Objectivity

An exploration of aspects of Bayesian multiple testing

Robust Bayesian analysis : sensitivity to the prior