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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

Book category Books in English Mathematics & science Mathematics Applied mathematics

• Full title: Inferential Models
• Author: Chuanhai Liu, Ryan Martin
• Language: English
• Binding: Hardback
• Number of pages: 256
• EAN: 9781439886489
• ISBN: 9781439886489
• ID: 10780632
• Publisher: Taylor & Francis Inc
• Weight: 590 g
• Dimensions: 165 × 241 × 21 mm
• Date of publishing: 25. September 2015

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