Ryan Martin, Chuanhai Liu

Inferential Models (eBook, PDF)

Reasoning with Uncertainty

• Format: PDF

Produktbeschreibung

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 meaning

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 276
• Erscheinungstermin: 25. September 2015
• Englisch
• ISBN-13: 9781439886519
• Artikelnr.: 43840505

Autorenporträt

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.

Inhaltsangabe

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.