Inferential Models
Reasoning with Uncertainty
1st Edition
Published on 25. September 2015
276 pages
978-1-040-05868-8 (ISBN)
Description
A New Approach to Sound Statistical ReasoningInferential Models: Reasoning with Uncertainty introduces the authors' recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The authors show how an IM produces meaning
Reviews / Votes
"The book . . . delivers on its promise. It should be read by all statisticians with an interest in the foundations and development of the statistical methods for inference."~Michael J. Lew, University of Melbourne
" . . . the book covers the motivations for the IM framework, the basic theory behind its calibration properties, a number of its applications and gives a new way of thinking compared to existing schools of thought on statistical inference"
~Apostolos Batsidis (Ioannina), Zentralblatt MATH "The book . . . delivers on its promise. It should be read by all statisticians with an interest in the foundations and development of the statistical methods for inference."
~Michael J. Lew, University of Melbourne
" . . . the book covers the motivations for the IM framework, the basic theory behind its calibration properties, a number of its applications and gives a new way of thinking compared to existing schools of thought on statistical inference"
~Apostolos Batsidis (Ioannina), Zentralblatt MATH
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
ISBN-13
978-1-040-05868-8 (9781040058688)
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Schweitzer Classification
Other editions
Additional editions
Book
12/2020
1st Edition
Chapman & Hall/CRC
€59.41
Shipment within 15-20 days
Book
09/2015
1st Edition
Chapman & Hall/CRC
€128.50
Shipment within 15-20 days
Persons
Ryan Martin is an associate professor in the Department of Mathematics, Statistics, and Computer Science at the University of Illinois at Chicago.
Chuanhai Liu is a professor in the Department of Statistics at Purdue University.
Chuanhai Liu is a professor in the Department of Statistics at Purdue University.
Content
Preliminaries. Prior-Free Probabilistic Inference. Two Fundamental Principles. Inferential Models. Predictive Random Sets. Conditional Inferential Models. Marginal Inferential Models. Normal Linear Models. Prediction of Future Observations. Simultaneous Inference on Multiple Assertions. Generalized Inferential Models. Future Research Topics. Bibliography. Index.
