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Statistical decision --- Decision making

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This book includes the lectures given during the third session of the School of Statistics for Astrophysics that took place at Autrans, near Grenoble, in France, in October 2017. The subject is Bayesian Methodology. The interest of this statistical approach in astrophysics probably comes from its necessity and its success in determining the cosmological parameters from observations, especially from the cosmic background luctuations. The cosmological community has thus been very active in this field for many years. But the Bayesian methodology, complementary to the more classical frequentist one, has many applications in physics in general due to its ability to incorporate a priori knowledge into inference, such as uncertainty brought by the observational processes. The Bayesian approach becomes more and more widespread in the astrophysical literature. This book contains statistics courses on basic to advanced methods with practical exercises using the R environment, by leading experts in their field. This covers the foundations of Bayesian inference, Markov chain Monte Carlo technique, model building, Approximate Bayesian Computation (ABC) and Bayesian nonparametric inference and clustering.

Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision

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Sequential methods in pattern recognition and machine learning

Machine elements --- Perceptrons. --- Statistical decision. --- Machine learning.

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Mathematical statistics --- Statistical decision --- Stochastic processes

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"Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand. This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kéry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks. The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB. Key Features: Contains a concise and gentle introduction to probability and applied statistics as needed in ecology and the environmental sciences. Covers the foundations of modern applied statistical modelling. Gives a comprehensive, applied introduction to what currently are the most widely used and most exciting, cutting-edge model fitting software packages: JAGS, NIMBLE, Stan, and TMB. Provides a highly accessible applied introduction to the two dominant methods of fitting parametric statistical models: maximum likelihood and Bayesian posterior inference. Details the principles of model building, model checking and model selection. Adopts a “Rosetta Stone” approach, wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in other engines. Provides all code available for download for students, at https://www-elsevier-com.proxy.lib.uwaterloo.ca/books-and-journals/book-companion/9780443137150 "--Provided by publisher.

Ecology --- Bayesian statistical decision theory. --- Statistical methods.