Inferential Models: Reasoning With Uncertainty (Monographs on Statistics and Applied Probability, 147, Band 147) - Hardcover

Buch 62 von 110: ISSN

Martin, Ryan; Liu, Chuanhai

 
9781439886489: Inferential Models: Reasoning With Uncertainty (Monographs on Statistics and Applied Probability, 147, Band 147)

Inhaltsangabe

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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Über die Autorin bzw. den Autor

Ryan Martin is an associate professor in the Department of Mathematics, Statistics, and Computer Science at the University of Illinois at Chicago.

Chuanhai Liu is a professor in the Department of Statistics at Purdue University.

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Weitere beliebte Ausgaben desselben Titels

9780367737801: Inferential Models: Reasoning With Uncertainty (Chapman & Hall/CRC Monographs on Statistics and Applied Prob)

Vorgestellte Ausgabe

ISBN 10:  0367737809 ISBN 13:  9780367737801
Verlag: Routledge, 2020
Softcover