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Inferential Models Reasoning with Uncertainty

Автор: Bo0mB0om » 12 февраля 2016
Inferential Models Reasoning with Uncertainty

Inferential Models: Reasoning with Uncertainty by Ryan Martin and Chuanhai Liu
English | 2015 | ISBN: 1439886482 | 276 pages | PDF | 9 MB

A New Approach to Sound Statistical Reasoning

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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