Inferential Models - Reasoning with Uncertainty (Paperback)

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.

General

Imprint:	Crc Press
Country of origin:	United Kingdom
Series:	Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Release date:	December 2020
First published:	2016
Authors:	Ryan Martin • Chuanhai Liu
Dimensions:	234 x 156mm (L x W)
Format:	Paperback
Pages:	256
ISBN-13:	978-0-367-73780-1
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Science & Mathematics > Mathematics > Applied mathematics > General Promotions
LSN:	0-367-73780-9
Barcode:	9780367737801

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