A First Course In Bayesian Statistical Methods

Author: Peter D. Hoff
Publisher: Springer Science & Business Media
ISBN: 9780387924076
Size: 35.53 MB
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A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

Methods In Neuroethological Research

Author: Hiroto Ogawa
Publisher: Springer Science & Business Media
ISBN: 4431543317
Size: 80.82 MB
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The rapid progress of neuroscience in the last decade can be largely attributed to significant advances in neuroethology, a branch of science that seeks to understand the neural basis of natural animal behavior. Novel approaches including molecular biological techniques, optical recording methods, functional anatomy, and informatics have brought drastic changes in how the neural systems underlying high-level behaviors such as learning and memory are described. This book introduces recent research techniques in neuroethology, with diverse topics involving nematodes, insects, and vertebrates (birds, mice and primates), divided into sections by research method. Each section consists of two chapters written by different authors who have developed their own unique approaches. Reports of research in “model animals” including C. elegans, Drosophila, and mice, which were not typical specimens in conventional neuroethology, have been deliberately selected for this book because a molecular genetic approach to these animals is necessary for advances in neuroethology. Novel methodology including optical recording and functional labeling with reporter genes such as GFP has been actively used in recent neurobiological studies, and genetic manipulation techniques such as optogenetics also are powerful tools for understanding the molecular basis of neural systems for animal behavior. This book provides not only these new strategies but also thought-provoking statements of philosophy in neuroethology for students and young researchers in the biological sciences.

A First Course In Multivariate Statistics

Author: Bernard Flury
Publisher: Springer Science & Business Media
ISBN: 1475727658
Size: 52.59 MB
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A comprehensive and self-contained introduction to the field, carefully balancing mathematical theory and practical applications. It starts at an elementary level, developing concepts of multivariate distributions from first principles. After a chapter on the multivariate normal distribution reviewing the classical parametric theory, methods of estimation are explored using the plug-in principles as well as maximum likelihood. Two chapters on discrimination and classification, including logistic regression, form the core of the book, followed by methods of testing hypotheses developed from heuristic principles, likelihood ratio tests and permutation tests. Finally, the powerful self-consistency principle is used to introduce principal components as a method of approximation, rounded off by a chapter on finite mixture analysis.

An Introduction To Bayesian Analysis

Author: Jayanta K. Ghosh
Publisher: Springer Science & Business Media
ISBN: 0387354336
Size: 79.27 MB
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This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior.

Monte Carlo Statistical Methods

Author: Christian Robert
Publisher: Springer Science & Business Media
ISBN: 1475730713
Size: 36.62 MB
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We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

Essential Statistical Inference

Author: Dennis D. Boos
Publisher: Springer Science & Business Media
ISBN: 1461448182
Size: 76.65 MB
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​This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology. Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods. ​

Bayesian Core A Practical Approach To Computational Bayesian Statistics

Author: Jean-Michel Marin
Publisher: Springer Science & Business Media
ISBN: 0387389792
Size: 51.87 MB
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This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications.

Introduction To Statistical Inference

Author: Jack C. Kiefer
Publisher: Springer Science & Business Media
ISBN: 146139578X
Size: 79.65 MB
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This book is based upon lecture notes developed by Jack Kiefer for a course in statistical inference he taught at Cornell University. The notes were distributed to the class in lieu of a textbook, and the problems were used for homework assignments. Relying only on modest prerequisites of probability theory and cal culus, Kiefer's approach to a first course in statistics is to present the central ideas of the modem mathematical theory with a minimum of fuss and formality. He is able to do this by using a rich mixture of examples, pictures, and math ematical derivations to complement a clear and logical discussion of the important ideas in plain English. The straightforwardness of Kiefer's presentation is remarkable in view of the sophistication and depth of his examination of the major theme: How should an intelligent person formulate a statistical problem and choose a statistical procedure to apply to it? Kiefer's view, in the same spirit as Neyman and Wald, is that one should try to assess the consequences of a statistical choice in some quan titative (frequentist) formulation and ought to choose a course of action that is verifiably optimal (or nearly so) without regard to the perceived "attractiveness" of certain dogmas and methods.