Inferential Models: Reasoning with Uncertainty

1st Edition

Ryan Martin, Chuanhai Liu

Chapman and Hall/CRC
Published September 25, 2015
Reference - 256 Pages - 37 B/W Illustrations
ISBN 9781439886489 - CAT# K14020
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability

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Summary

A New Approach to Sound Statistical Reasoning

Inferential 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 meaningful prior-free probabilistic inference at a high level.

The book covers the foundational motivations for this new IM approach, the basic theory behind its calibration properties, a number of important applications, and new directions for research. It discusses alternative, meaningful probabilistic interpretations of some common inferential summaries, such as p-values. It also constructs posterior probabilistic inferential summaries without a prior and Bayes’ formula and offers insight on the interesting and challenging problems of conditional and marginal inference.

This book delves into statistical inference at a foundational level, addressing what the goals of statistical inference should be. It explores a new way of thinking compared to existing schools of thought on statistical inference and encourages you to think carefully about the correct approach to scientific inference.

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