A differential approach to inference in Bayesian networks
From MaRDI portal
Publication:3452493
DOI10.1145/765568.765570zbMath1325.68226arXiv1301.3847OpenAlexW2153074847MaRDI QIDQ3452493
Publication date: 12 November 2015
Published in: Journal of the ACM (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1301.3847
Related Items (53)
An extension of the differential approach for Bayesian network inference to dynamic Bayesian networks ⋮ Decision-network polynomials and the sensitivity of decision-support models ⋮ A Differential Approach for Staged Trees ⋮ Efficient sensitivity analysis in hidden Markov models ⋮ Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference ⋮ On probabilistic inference by weighted model counting ⋮ A simplified matrix formulation for sensitivity analysis of hidden Markov models ⋮ Exact stochastic constraint optimisation with applications in network analysis ⋮ Connecting Width and Structure in Knowledge Compilation (Extended Version) ⋮ Fast structured prediction using large margin sigmoid belief networks ⋮ A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams ⋮ The YODO algorithm: an efficient computational framework for sensitivity analysis in Bayesian networks ⋮ Coherent combination of probabilistic outputs for group decision making: an algebraic approach ⋮ Portfolios in stochastic local search: efficiently computing most probable explanations in Bayesian networks ⋮ Discriminative training of feed-forward and recurrent sum-product networks by extended Baum-Welch ⋮ Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks ⋮ Equivalence classes of staged trees ⋮ Logical Compilation of Bayesian Networks with Discrete Variables ⋮ Probabilistic inference with noisy-threshold models based on a CP tensor decomposition ⋮ The complexity of Bayesian networks specified by propositional and relational languages ⋮ Learning multi-linear representations of distributions for efficient inference ⋮ Probabilistic decision graphs for optimization under uncertainty ⋮ Robustifying sum-product networks ⋮ Direct causal structure extraction from pairwise interaction patterns in NAT modeling Bayesian networks ⋮ Visualizing and understanding sum-product networks ⋮ Case-factor diagrams for structured probabilistic modeling ⋮ Resolving Inconsistencies of Scope Interpretations in Sum-Product Networks ⋮ On Converting Sum-Product Networks into Bayesian Networks ⋮ Probabilistic decision graphs for optimization under uncertainty ⋮ Importance sampling-based estimation over AND/OR search spaces for graphical models ⋮ SampleSearch: importance sampling in presence of determinism ⋮ Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers ⋮ Compiling relational Bayesian networks for exact inference ⋮ Discovery of statistical equivalence classes using computer algebra ⋮ Understanding the scalability of Bayesian network inference using clique tree growth curves ⋮ On-line alert systems for production plants: A conflict based approach ⋮ Formulating Asymmetric Decision Problems as Decision Circuits ⋮ Sensitivity analysis in multilinear probabilistic models ⋮ Learning to assign degrees of belief in relational domains ⋮ Sensitivity analysis beyond linearity ⋮ A hierarchy of sum-product networks using robustness ⋮ On the relative expressiveness of Bayesian and neural networks ⋮ Sum-product graphical models ⋮ Learning directed acyclic graph SPNs in sub-quadratic time ⋮ Unnamed Item ⋮ AND/OR search spaces for graphical models ⋮ Unnamed Item ⋮ \textsc{Strudel}: A fast and accurate learner of structured-decomposable probabilistic circuits ⋮ Conditional sum-product networks: modular probabilistic circuits via gate functions ⋮ Learning tractable Bayesian networks in the space of elimination orders ⋮ Connecting knowledge compilation classes and width parameters ⋮ A geometric characterization of sensitivity analysis in monomial models ⋮ A differential semantics for jointree algorithms
This page was built for publication: A differential approach to inference in Bayesian networks