Case Studies In Bayesian Statistics (lecture Notes In Statistics)
by Nozer D. Singpurwalla /
1993 / English / PDF
11.8 MB Download
The past few years have witnessed dramatic advances in
computational methods for Bayesian inference. As a result, Bayesian
approaches to solving a wide variety of problems in data analysis
and decision-making have become feasible, and there is currently a
growth spurt in the application of Bayesian methods. The purpose of
this volume is to present several detailed examples of applications
of Bayesian thinking, with an emphasis on the scientific or
technological context of the problem being solved. The papers
collected here were presented and discussed at a Workshop held at
Carnegie-Mellon University, September 29 through October 1, 1991.
There are five ma jor articles, each with two discussion pieces
and a reply. These articles were invited by us following a public
solicitation of abstracts. The problems they address are diverse,
but all bear on policy decision-making. Though not part of our
original design for the Workshop, that commonality of theme does
emphasize the usefulness of Bayesian meth ods in this arena. Along
with the invited papers were several additional commentaries of a
general nature; the first comment was invited and the remainder
grew out of the discussion at the Workshop. In addition there are
nine contributed papers, selected from the thirty-four presented at
the Workshop, on a variety of applications. This collection of case
studies illustrates the ways in which Bayesian methods are being
incorporated into statistical practice. The strengths (and
limitations) of the approach become apparent through the examples.
The past few years have witnessed dramatic advances in
computational methods for Bayesian inference. As a result, Bayesian
approaches to solving a wide variety of problems in data analysis
and decision-making have become feasible, and there is currently a
growth spurt in the application of Bayesian methods. The purpose of
this volume is to present several detailed examples of applications
of Bayesian thinking, with an emphasis on the scientific or
technological context of the problem being solved. The papers
collected here were presented and discussed at a Workshop held at
Carnegie-Mellon University, September 29 through October 1, 1991.
There are five ma jor articles, each with two discussion pieces
and a reply. These articles were invited by us following a public
solicitation of abstracts. The problems they address are diverse,
but all bear on policy decision-making. Though not part of our
original design for the Workshop, that commonality of theme does
emphasize the usefulness of Bayesian meth ods in this arena. Along
with the invited papers were several additional commentaries of a
general nature; the first comment was invited and the remainder
grew out of the discussion at the Workshop. In addition there are
nine contributed papers, selected from the thirty-four presented at
the Workshop, on a variety of applications. This collection of case
studies illustrates the ways in which Bayesian methods are being
incorporated into statistical practice. The strengths (and
limitations) of the approach become apparent through the examples.