In the concluding chapter of his famous book on the theory of evolution by natural selection, Charles Darwin (1859) remarked that: When the views entertained in this volume on the origin of species, or when analogous views are generally admitted, we can dimly foresee that there will be a considerable revolution in natural history. This proved, of course, to be completely correct. At present there is a great divergence of opinion about the general importance of natural selection in the evolutionary process. Nevertheless, biologists are, on the whole, united in their acceptance of the potential power of selection in changing populations. Given this situation, it is not surprising to find that many attempts to detect the effects of natural selection have been made since the time of Darwin. This area of study has been called ecological genetics. It involves the collection of data of various kinds and, in many cases, the development of special methods for analysing these data. This book is a summary of methods for data analysis, concentrating on those that are applicable to animal populations, particularly wild populations.
Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.
During the Second World War, just under 2000 British citizens were detained without charge, trial or term set, under Regulation 18B of the wartime Defence Regulations. This book provides a comprehensive study of Regulation 18B and its precursor in the First World War, Regulation 14B.
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