Brian J. Reich – författare

Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear regression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) samplingModel-comparison and goodness-of-fit measures, including sensitivity to priors.To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:Handling of missing and censored dataPriors for high-dimensional regression modelsMachine learning models including Bayesian adaptive regression trees and deep learningComputational techniques for large datasetsFrequentist properties of Bayesian methods.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 is made available on the book’s website.

Statistics of extremes is a prominent field of research concerned with modeling the risk of occurrence of extreme events, that is, low-probability-high-impact events such as a stock market crash, hurricanes, heatwaves, and widespread flooding.The Handbook of Statistics of Extremes covers statistical models for univariate, multivariate, and spatio-temporal extreme values. Written by leading experts from around the world, it serves as a key reference for statisticians and data scientists, as well as for professionals working in risk modeling—such as geophysical and climate scientists, financial analysts, and health clinicians and neuroscientists—and as a valuable resource for practitioners and graduate students who wish to deepen their understanding of the statistical modeling of extreme events.Key Features:· Presents frequentist and Bayesian methods, as well as AI-based techniques for extreme value analysis.· Details how to model the frequency, magnitude, and spatio-temporal dependence of extreme events, and how to extrapolate into the tails of a distribution beyond observed data.· Provides code, data, and other additional materials available here: https://extremestats.github.io/Handbook/.

Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear regression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of 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) sampling Model-comparison and goodness-of-fit measures, including sensitivity to priors. To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics: Handling of missing and censored data Priors for high-dimensional regression models Machine learning models including Bayesian adaptive regression trees and deep learning Computational techniques for large datasets Frequentist properties of Bayesian methods. 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 is made available on the book's website.