Probability propagation
From MaRDI portal
Publication:1356197
DOI10.1007/BF01531015zbMath0875.68676OpenAlexW4254018622MaRDI QIDQ1356197
Glenn R. Shafer, Prakash P. Shenoy
Publication date: 9 November 1997
Published in: Annals of Mathematics and Artificial Intelligence (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf01531015
Related Items
Potential influence diagrams, A comparison of graphical techniques for decision analysis, Soft evidential update for probabilistic multiagent systems, Classification using hierarchical naïve Bayes models, Fault Tolerance in Belief Formation Networks, Graphical Models and Message-Passing Algorithms: Some Introductory Lectures, Unnamed Item, Importance sampling algorithms for the propagation of probabilities in belief networks, Irrelevance and parameter learning in Bayesian networks, Linear Belief Functions for Data Analytics, Making inferences in incomplete Bayesian networks: a Dempster-Shafer belief function approach, Being Bayesian about learning Gaussian Bayesian networks from incomplete data, Mixtures of truncated basis functions, Regularized Gaussian belief propagation, An axiomatic framework for influence diagram computation with partially ordered preferences, Independence of causal influence and clique tree propagation, Probabilistic decision graphs for optimization under uncertainty, Unifying tree decompositions for reasoning in graphical models, Partition-based logical reasoning for first-order and propositional theories, Fusion and propagation with multiple observations in belief networks, A machine learning approach to algorithm selection for \(\mathcal{NP}\)-hard optimization problems: a case study on the MPE problem, Probabilistic decision graphs for optimization under uncertainty, Topological parameters for time-space tradeoff, Anytime anyspace probabilistic inference, Classification using hierarchical Naïve Bayes models, Learning mixtures of truncated basis functions from data, New Advances in Logic-Based Probabilistic Modeling by PRISM, A simple graphical approach for understanding probabilistic inference in Bayesian networks, Embedding Logics in the Local Computation Framework, LEARNING BAYESIAN NETWORK PARAMETERS FROM SOFT DATA, Perspectives on the theory and practice of belief functions, Learning Bayesian networks from incomplete data with the node-average likelihood, A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques, Bucket renormalization for approximate inference
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Probability functions on complex pedigrees
- Fusion, propagation, and structuring in belief networks
- Propagating belief functions in qualitative Markov trees
- Network-based heuristics for constraint-satisfaction problems
- Triangulated graphs and the elimination process
- Nonserial dynamic programming
- On the Desirability of Acyclic Database Schemes
- Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs
- Complexity of Finding Embeddings in a k-Tree
