Ryan Martin, Chuanhai Liu

Inferential Models (eBook, PDF)

Reasoning with Uncertainty

• Format: PDF

Produktbeschreibung

This book 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 book covers the foundational motivations for this new approach, the basic theory behind its calibration properties, many important applications, and new directions for research. It explores a new way of thinking compared to existing schools of thought on statistical inference and encourages readers to think carefully about the correct approach to scientific inference.

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Produktdetails

• Verlag: Taylor & Francis
• 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.