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Book synopsis

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.

Book details

• Full title: Inferential Models
• Author: Ryan Martin, Chuanhai Liu
• Language: English
• Binding: Paperback
• Number of pages: 256
• EAN: 9780367737801
• ISBN: 0367737809
• ID: 33479217
• Publisher: Taylor & Francis Ltd
• Weight: 510 g
• Dimensions: 234 × 156 mm
• Date of publishing: 18. December 2020

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