Name: Making decisions with evidential probability and objective Bayesian calibration inductive logics
Keywords: Logic in artificial intelligence, info:eu-repo/classification/ddc/100, Learning and adaptive systems in artificial intelligence, decision under uncertainty, objective Bayesianism, Decision under uncertainty, frequentist statistics, Objective Bayesianism, Agent-based modelling; Decision under uncertainty; Frequentist statistics; Imprecise probability; Machine learning; Objective Bayesianism;, agent-based modelling

descriptionPublicationkeyboard_double_arrow_right Article 01 Nov 2023 English Publisher:Elsevier BVJournal:International Journal of Approximate Reasoning, volume 162, page 109,030 (issn: 0888-613X,

Authors: Radzvilas, Mantas; Peden, William; De Pretis, Francesco;

doi: 10.1016/j.ijar.2023.109030

handle: 11380/1320111

Making decisions with evidential probability and objective Bayesian calibration inductive logics

- Summary
- Subjects
- Metrics

Abstract

zbMATH Open Web Interface contents unavailable due to conflicting licenses.

Related Organizations

University of Konstanz
Germany
Department of Philosophy
United States
Lingnan University
University of Modena and Reggio Emilia
Italy
Lingnan University
China (People's Republic of)

View all View all

Keywords

Logic in artificial intelligence, info:eu-repo/classification/ddc/100, Learning and adaptive systems in artificial intelligence, decision under uncertainty, objective Bayesianism, Decision under uncertainty, frequentist statistics, Objective Bayesianism, Agent-based modelling; Decision under uncertainty; Frequentist statistics; Imprecise probability; Machine learning; Objective Bayesianism;, agent-based modelling, Reasoning under uncertainty in the context of artificial intelligence, Probability and inductive logic, Agent-based modelling, imprecise probability, Frequentist statistics, machine learning, Machine learning, Imprecise probability

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	2
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average