Inferential Models

Regular price €56.99
A01=Chuanhai Liu
A01=Ryan Martin
Age Group_Uncategorized
Age Group_Uncategorized
Author_Chuanhai Liu
Author_Ryan Martin
automatic-update
auxiliary
Auxiliary Variable
auxiliary variables
Baseline Association
Bayesian inference
belief function
Category1=Non-Fiction
Category=JMB
Category=PBT
Category=PS
COP=United Kingdom
coverage
Coverage Probability
Coverage Probability Results
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Distribution Function
eq_isMigrated=2
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eq_science
eq_society-politics
Family-wise Error Rate
Fiducial Argument
fiducial inference
Fiducial Interval
Focal Elements
foundations of statistics
frequentist inference
function
Generalized Inferential Models
Genome Wide Association Studies
IM Approach
Inferential Output
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Jeffreys Prior
Jeffreys Rule
Language_English
Marginal Association
Marginal Inference
Maximum Marginal Likelihood Estimator
Minimal Sufficient Statistic
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PDE Approach
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plausibility
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Post-selection Inference
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Prior-Free Probabilistic Inference
probability
PS=Active
random
random set
sampling
scientific inference
set
softlaunch
statistical inference
variable

Product details

  • ISBN 9780367737801
  • Weight: 360g
  • Dimensions: 156 x 234mm
  • Publication Date: 18 Dec 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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A 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 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.

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.