Inferential Models : Reasoning with Uncertainty Paperback / softback
by Ryan Martin, Chuanhai Liu
Part of the Chapman & Hall/CRC Monographs on Statistics and Applied Probability series
Paperback / softback
Description
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.
Information
-
Out of stock
- Format:Paperback / softback
- Pages:256 pages
- Publisher:Taylor & Francis Ltd
- Publication Date:18/12/2020
- Category:
- ISBN:9780367737801
Other Formats
- Hardback from £84.99
- PDF from £41.39
Information
-
Out of stock
- Format:Paperback / softback
- Pages:256 pages
- Publisher:Taylor & Francis Ltd
- Publication Date:18/12/2020
- Category:
- ISBN:9780367737801
