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Inferential Models

Reasoning with Uncertainty
BuchGebunden
256 Seiten
Englisch
CRC Presserschienen am25.09.2015
A New Approach to Sound Statistical ReasoningInferential 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.mehr
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Produkt

KlappentextA New Approach to Sound Statistical ReasoningInferential 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.
Details
ISBN/GTIN978-1-4398-8648-9
ProduktartBuch
EinbandartGebunden
FormatGenäht
Verlag
Erscheinungsjahr2015
Erscheinungsdatum25.09.2015
Seiten256 Seiten
SpracheEnglisch
MasseBreite 160 mm, Höhe 244 mm, Dicke 22 mm
Gewicht537 g
Artikel-Nr.34618210

Inhalt/Kritik

Inhaltsverzeichnis
Preliminaries. Prior-Free Probabilistic Inference. Two Fundamental Principles. Inferential Models. Predictive Random Sets. Conditional Inferential Models. Marginal Inferential Models. Normal Linear Models. Prediction of Future Observations. Simultaneous Inference on Multiple Assertions. Generalized Inferential Models. Future Research Topics. Bibliography. Index.mehr

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.