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About this product
- DescriptionA 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 t 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.
- Author BiographyRyan 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.
- Author(s)Chuanhai Liu,Ryan Martin
- PublisherTaylor & Francis Inc
- Date of Publication01/10/2015
- Series TitleChapman & Hall/CRC Monographs on Statistics & Applied Probability
- Series Part/Volume Number145
- Place of PublicationBosa Roca
- Country of PublicationUnited States
- ImprintCRC Press Inc
- Content Note37 black & white illustrations, 10 black & white tables
- Weight544 g
- Width156 mm
- Height235 mm
- Spine20 mm
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