Livros

Computational Bayesian Statistics - An Introduction (2019)

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

Distance Sampling: Methods and Applications (2015)

Distance sampling refers to a suite of methods, including line and point transect sampling, in which animal density or abundance is estimated from a sample of distances to detected individuals. The book illustrates these methods through case studies; data sets and computer code are supplied to readers through the book’s accompanying website. Some of the case studies use the software Distance, while others use R code. The book is in three parts. The first part addresses basic methods, the design of surveys, distance sampling experiments, field methods and data issues. The second part develops a range of modelling approaches for distance sampling data. The third part describes variations in the basic method; discusses special issues that arise when sampling different taxa (songbirds, seabirds, cetaceans, primates, ungulates, butterflies, and plants); considers advances to deal with failures of the key assumptions; and provides a check-list for those conducting surveys.

Non-Linear Time Series: Extreme Events and Integer Value Problems (2014)

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included. Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time series.

Estatística Bayesiana Computacional – Uma Introdução (2015)

A Estatística Bayesiana é uma abordagem à Inferência e Decisão Estatísticas alicerçada na conhecida fórmula de Bayes, e que se tem desenvolvido extraordinariamente a partir da década de 80, a tal ponto que o seu uso na análise de dados rompeu praticamente todas as fronteiras dos vários domínios do conhecimento científico. Este livro numa 1ª fase descreve os fundamentos dessa abordagem estatística e a representação da informação apriorística para complementar a especificação do modelo bayesiano a analisar. Segue-se-lhe uma descrição mais minuciosa dos principais procedimentos de análise aposteriorística e a sua aplicação em moldes exatos ou aproximados, no sentido analítico ou numérico, a problemas com modelos gaussianos e discretos. A fase seguinte inicia a via da simulação estocástica com o estudo dos métodos de Monte Carlo (MC) tradicionais e a complementação de aspetos inferenciais, como a avaliação de modelos que requer dominantemente o uso de tal via. Prossegue-se então com o estudo de métodos MC mais abrangentes, apoiados na simulação de cadeias de Markov convergentes para as distribuições aposteriorísticas de interesse. A este respeito, descrevem-se primeiro os principais algoritmos e depois alguns aspetos específicos relacionados com a sua implementação, aplicabilidade, e extensão a quadros mais vastos. A profusa ilustração do emprego dos métodos MC em cadeias markovianas (MCMC) - e de outros, anteriormente descritos - está bem patente quer em ambas as situações referidas quer no ómega capitular do livro, o qual é inteiramente dedicado a análises de problemas reais emanados de vários campos de aplicação estatística.