Possibilistic and probabilistic likelihood functions and their extensions: common features and specific characteristics
From MaRDI portal
Publication:279392
DOI10.1016/j.fss.2013.09.010zbMath1334.60004OpenAlexW2008442174MaRDI QIDQ279392
Davide Petturiti, Barbara Vantaggi, Giulianella Coletti
Publication date: 28 April 2016
Published in: Fuzzy Sets and Systems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.fss.2013.09.010
coherenceconditional probabilitylikelihood function\(T\)-conditional possibilityDP-conditional possibility
Related Items (14)
DAG representation of asymmetric independence models arising in coherent conditional possibility theory ⋮ Probabilistic squares and hexagons of opposition under coherence ⋮ Fuzzy memberships as likelihood functions in a possibilistic framework ⋮ Consequences of the minimum specificity principle on conditioning and on independence in possibility theory ⋮ Finitely maxitive conditional possibilities, Bayesian-like inference, disintegrability and conglomerability ⋮ Likelihood-fuzzy analysis: from data, through statistics, to interpretable fuzzy classifiers ⋮ Probabilistic inferences from conjoined to iterated conditionals ⋮ Envelopes of conditional probabilities extending a strategy and a prior probability ⋮ When upper conditional probabilities are conditional possibility measures ⋮ Bayesian Updating under Incomplete or Imprecise Information in Finite Spaces ⋮ Coherent T-conditional Possibility Envelopes and Nonmonotonic Reasoning ⋮ Bayesian inference: the role of coherence to deal with a prior belief function ⋮ Decision Rules Under Vague and Uncertain Information ⋮ Finitely maxitive \(T\)-conditional possibility theory: coherence and extension
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Generalized Bayesian inference in a fuzzy context: from theory to a virtual reality application
- Inferential processes leading to possibility and necessity
- Algorithms for possibility assessments: coherence and extension
- Inferential models and relevant algorithms in a possibilistic framework
- Lower previsions induced by multi-valued mappings
- Jeffrey's rule of conditioning in a possibilistic framework
- Soft methods for handling variability and imprecision. Selected papers based on the presentations at the 4th international conference on soft methods in probability and statistics (SMPS'2008), Toulouse, France, September 8--10, 2008.
- Joint propagation of probability and possibility in risk analysis: towards a formal framework
- Possibility theory and statistical reasoning
- Conditional probability and fuzzy information
- \(T\)-conditional possibilities: coherence and inference
- Fuzzy sets and statistical data
- When upper probabilities are possibility measures
- A semantics for possibility theory based on likelihoods
- Integrated likelihood methods for eliminating nuisance parameters. (With comments and a rejoinder).
- Independence and possibilistic conditioning
- Stochastic independence in a coherent setting
- Toward a perception-based theory of probabilistic reasoning with imprecise probabilities
- Supremum preserving upper probabilities
- Do inferential processes affect uncertainty frameworks?
- Statistical matching of multiple sources: A look through coherence
- Possibility theory: conditional independence
- Toward a generalized theory of uncertainty (GTU) -- an outline
- On possibilistic inference
- Generalized information theory
- Statistical Methods for Fuzzy Data
- POSSIBILITY THEORY III: POSSIBILISTIC INDEPENDENCE
- Integrated Likelihood in a Finitely Additive Setting
- Finitely additive conditional probabilities
- Probabilistic logic under coherence, model-theoretic probabilistic logic, and default reasoning in SystemP
- Coherent numerical and ordinal probabilistic assessments
- Independence and conditional possibility for strictly monotone triangular norms
- Representation of conditional probability measures on Boolean algebras
- The role of coherence in eliciting and handling imprecise probabilities and its application to medical diagnosis
This page was built for publication: Possibilistic and probabilistic likelihood functions and their extensions: common features and specific characteristics
