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

M.J. (Susie) Bayarri

Conditioning is the issue

The Climate Action Reserve (Reserve) published its Mexico Forest Protocol Version 2.0 (MFP V2.0) in March 2020. While the Reserve intends for the MFP 2.0 to be a complete, transparent document, it recognizes that correction of errors and clarifications will be necessary as the protocol is implemented and issues are identified. This document is an official record of all errata and clarifications applicable to the MFP 2.0.

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Errata and Clarifications

Invited Discussion of the paper ``Bayesian Models for Variable Selection that Incorporate Biological Information'', by Vannucci, M, and Stingo, FC

After a general discussion of some basic issues in Bayesian model selection, we briefly review three fairly recent developments: (i) The median probability model (rather than the highest posterior probability model) is the model which is typically optimal for prediction in variable selection problems; discussion of this highlights the central role of the overall posterior probability with which a variable occurs in one of the candidate models, called the “posterior inclusion probability.” (ii) When the space of models is large, search strategies need to be carefully developed for exploration of the space; an appealing strategy is to perform a stochastic search that (roughly) adds or removes variables based on their current estimated posterior inclusion probability. (iii) Expected posterior priors provide a constructive solution to the problem of choosing prior distributions for model selection, priors that are appropriately ‘calibrated’ across the parameters of different models. Illustrations to linear mo...

James O. Berger

Papers

M.J. (Susie) Bayarri

Conditioning is the issue

Errata and Clarifications

Invited Discussion of the paper ``Bayesian Models for Variable Selection that Incorporate Biological Information'', by Vannucci, M, and Stingo, FC

Some Recent Developments in Bayesian Variable Selection