Inferential Models Reasoning with Uncertainty

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. | Inferential Models Reasoning with Uncertainty