Inferential Models

<p><em>A New Approach to Sound Statistical Reasoning</em></p><p></p><p><strong>Inferential Models: Reasoning with Uncertainty</strong> 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.</p><p></p><p>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 <i>p</i>-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. </p><p></p><p>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.</p>