A Mathematical Theory of Evidence
- G. Shafer
- MathematicsA Mathematical Theory of Evidence
- 30 June 2020
This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
A tutorial on conformal prediction
This tutorial presents a self-contained account of the theory of conformal prediction and works through several numerical examples of how the model under which successive examples are sampled independently from the same distribution can be applied to any method for producing ŷ.
Axioms for probability and belief-function proagation
- P. P. Shenoy, G. Shafer
- Computer Science, MathematicsConference on Uncertainty in Artificial…
- 1 June 1990
This paper describes an abstract framework and axioms under which exact local computation of marginals is possible and shows how the problem of computing marginals of joint probability distributions and joint belief functions fits the general framework.
Probability and Finance: It's Only a Game!
Preface. Probability and Finance as a Game. PROBABILITY WITHOUT MEASURE. The Historical Context. The Bounded Strong Law of Large Numbers. Kolmogorov's Strong Law of Large Numbers. The Law of the…
The art of causal conjecture
- G. Shafer
- Philosophy
- 1996
The Art of Causal Conjecture shows that causal ideas can be equally important in theory and by bringing causal ideas into the foundations of probability allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.
Algorithmic Learning in a Random World
- V. Vovk, A. Gammerman, G. Shafer
- Computer Science
- 22 March 2005
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness and describes how several important machine learning problems cannot be solved if the only assumption is randomness.
Perspectives on the theory and practice of belief functions
- G. Shafer
- PhilosophyInternational Journal of Approximate Reasoning
- 1 September 1990
Probability propagation
- G. Shafer, P. P. Shenoy
- MathematicsAnnals of Mathematics and Artificial Intelligence
- 1990
The account given here avoids the divisions required by conditional probabilities and generalizes readily to alternative measures of subjective probability, such as Dempster-Shafer or Spohnian belief functions.
The Enterprise of Knowledge: An Essay on Knowledge, Credal Probability, and Chance
- G. Shafer
- Philosophy
- 1 May 1982
This book presents a major conceptual and speculative philosophic investigation of knowledge, belief, and decision. It offers a distinctive approach to the improvement of knowledge where knowledge is…
The Language of Betting as a Strategy for Statistical and Scientific Communication
- G. Shafer
- Computer Science
- 16 March 2019
A simple betting interpretation of likelihood ratios is called on, which leads to methods that lend themselves to meta-analysis and accounting for multiple testing, and does not encourage the fallacy that probabilistic models imply the existence of unseen alternative worlds.
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