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Bayesian Core A Practical Approach to Computational Bayesian Statistics
The purpose of this book is to provide a self-contained (we insist!) entry into
practical and computational Bayesian statistics using generic examples from
the most common models for a class duration of about seven blocks that
roughly correspond to 13 to 15 weeks of teaching (with three hours of lectures
per week), depending on the intended level and the prerequisites imposed on
the students. (That estimate does not include practice—i.e., programming
labs—since those may have a variable duration, also depending on the students’
involvement and their programming abilities.) The emphasis on practice
is a strong feature of this book in that its primary audience consists of graduate
students who need to use (Bayesian) statistics as a tool to analyze their
experiments and/or datasets. The book should also appeal to scientists in all
fields, given the versatility of the Bayesian tools. It can also be used for a more
classical statistics audience when aimed at teaching a quick entry to Bayesian
statistics at the end of an undergraduate program for instance. (Obviously, it
can supplement another textbook on data analysis at the graduate level.)
The format of the book is of a rather sketchy coverage of the topics, always
backed by a motivated problem and a corresponding dataset (available
on the Website of the course), and a detailed resolution of the inference procedures
pertaining to this problem, sometimes including commented R programs.
Special attention is paid to the derivation of prior distributions, and
operational reference solutions are proposed for each model under study. Additional
cases are proposed as exercises. The spirit is not unrelated to that of
viii Preface
Nolan and Speed (2000), with more emphasis on the theoretical and methodological
backgrounds. We originally planned a complete set of lab reports,
but this format would have forced us both to cut on the methodological side
and to increase the description of the datasets and the motivations for their
analysis. The current format is therefore more self-contained (than it would
have been in the lab scenario) and can thus serve as a unique textbook for a
service course for scientists aimed at analyzing data the Bayesian way or as
an introductory course on Bayesian statistics.
1 User’s Manual
2 Normal Models . .
3 Regression and Variable Selection
4 Generalized Linear Models
5 Capture–Recapture Experiments .
6 Mixture Models
7 Dynamic Models.
8 Image Analysis
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