On the public citation graph

Learning non probabilistic belief networks

https://doi.org/10.1007/bfb0028182
0 signed · 26 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/int.4550050302openalex-snapshot
The concept of conditional fuzzy measure
inferredcites →https://doi.org/10.1007/3-540-54563-8openalex-snapshot
Methodologies for Intelligent Systems
inferredcites →https://doi.org/10.1007/3-540-54659-6_73openalex-snapshot
Learning with CASTLE
inferredcites →https://doi.org/10.1007/978-3-540-44792-4_20openalex-snapshot
Axioms for Probability and Belief-Function Propagation
inferredcites →https://doi.org/10.1007/bf00994110openalex-snapshot
A Bayesian Method for the Induction of Probabilistic Networks from Data
inferredcites →https://doi.org/10.1016/0004-3702(87)90068-3openalex-snapshot
Bayesian and non-bayesian evidential updating
inferredcites →https://doi.org/10.1016/b978-1-55860-203-8.50055-3openalex-snapshot
Detecting Causal Relations in the Presence of Unmeasured Variables
inferredcites →https://doi.org/10.1016/c2009-0-08670-xopenalex-snapshot
Uncertainty in Artificial Intelligence
inferredcites →https://doi.org/10.1016/c2013-0-12606-2openalex-snapshot
Uncertainty in Artificial Intelligence
inferredcites →https://doi.org/10.1093/ej/31.121.133openalex-snapshot
Journal of the Royal Statistical Society.
inferredcites →https://doi.org/10.1109/tit.1968.1054142openalex-snapshot
Approximating discrete probability distributions with dependence trees
inferredcites →https://doi.org/10.1111/j.2517-6161.1988.tb01721.xopenalex-snapshot
Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems
inferredcites →https://doi.org/10.3233/fi-2010-241openalex-snapshot
Methodologies for Intelligent Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/(sici)1098-111x(199707)12:7<495::aid-int2>3.0.co;2-gopenalex-snapshot
On the use of independence relationships for learning simplified belief networks
inferred← citeshttps://doi.org/10.1007/3-540-60112-0_10openalex-snapshot
Axiomatic treatment of possibilistic independence
inferred← citeshttps://doi.org/10.1007/978-3-7091-2690-5_8openalex-snapshot
Independence Relationships in Possibility Theory and Their Application to Learning Belief Networks
inferred← citeshttps://doi.org/10.1007/978-94-017-1733-5_9openalex-snapshot
Independence in Uncertainty Theories and Its Applications to Learning Belief Networks
inferred← citeshttps://doi.org/10.1007/bfb0028174openalex-snapshot
Symbolic and Quantitative Approaches to Reasoning and Uncertainty
inferred← citeshttps://doi.org/10.1007/bfb0035951openalex-snapshot
Probabilistic consistency of conditional probability bounds
inferred← citeshttps://doi.org/10.1016/j.ijar.2004.10.003openalex-snapshot
Graphical models for imprecise probabilities
inferred← citeshttps://doi.org/10.1016/s0165-0114(97)00160-7openalex-snapshot
Independence concepts in possibility theory: Part I
inferred← citeshttps://doi.org/10.1016/s0165-0114(97)00161-9openalex-snapshot
Independence concepts in possibility theory: Part II
inferred← citeshttps://doi.org/10.1016/s0888-613x(98)00010-3openalex-snapshot
A parallel algorithm for building possibilistic causal networks
inferred← citeshttps://doi.org/10.1016/s0888-613x(98)00012-7openalex-snapshot
Possibilistic conditional independence: A similarity-based measure and its application to causal network learning
inferred← citeshttps://doi.org/10.1023/a:1008375228885openalex-snapshot
Application of Bayesian Network Learning Methods to Waste Water Treatment Plants
inferred← citeshttps://doi.org/10.1080/095281398146743openalex-snapshot
Independency relationships and learning algorithms for singly connected networks
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.