| dc.description.abstract | The purpose of this book is to provide a self-contained 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 13–15 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., R programming labs, writing
data reports—since those may have a variable duration, also depending on
the students’ involvement and their programming abilities.) The emphasis on
practice is a strong commitment 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 who want to engage into Bayesian statistics, given
the versatility of the Bayesian tools. Bayesian essentials 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.) | en_US |
