Learning Bayesian networks for discrete data

DOI10.1016/j.csda.2008.10.007zbMath1452.62086OpenAlexW2166177483MaRDI QIDQ961206

Faming Liang, Jian Zhang

Publication date: 30 March 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.10.007

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items (5)

Weak Convergence Rates of Population Versus Single-Chain Stochastic Approximation MCMC Algorithms ⋮ Trajectory averaging for stochastic approximation MCMC algorithms ⋮ Parallel and interacting stochastic approximation annealing algorithms for global optimisation ⋮ Polyhedral approaches to learning Bayesian networks ⋮ The Bayesian method for causal discovery of latent-variable models from a mixture of experimental and observational data

Uses Software

• TETRAD

Cites Work

• Unnamed Item
• On the use of stochastic approximation Monte Carlo for Monte Carlo integration
• Causation, prediction, and search
• A Bayesian method for the induction of probabilistic networks from data
• Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
• A new approach for learning belief networks using independence criteria
• Stochastic approximation and its applications
• Learning Bayesian networks: The combination of knowledge and statistical data
• Graphical and Recursive Models for Contingency Tables
• Learning Causal Bayesian Network Structures From Experimental Data
• Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• Dynamic weighting in Monte Carlo and optimization
• A Theory for Dynamic Weighting in Monte Carlo Computation
• Dynamically Weighted Importance Sampling in Monte Carlo Computation
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• Bayesian Graphical Models for Discrete Data
• Decomposable graphical Gaussian model determination
• Stochastic Approximation in Monte Carlo Computation
• Stability of Stochastic Approximation under Verifiable Conditions
• A Stochastic Approximation Method

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