Learning Bayesian Networks: Search Methods and Experimental Results

By M. Chickering, Dan Geiger, David E. Heckerman. 1995

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “Examples of modular scores include the BDe score (Heckerman et al., 1995) and the BGe score (Geiger and Heckerman, 1994; Kuipers et al., 2014).”
The max-min hill-climbing Bayesian network structure learning algorithm (2006, methodology): “For instance, random restarts, simulated annealing, or incorporation of a TABU list are often added to a search procedure (Chickering et al., 1995; Bouckaert, 1995).”
A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests (2006, methodology): “…due to the exponentially large size of the search space, there is a growing interest in other heuristic search methods such as simulated annealing (Chickering et al., 1995), tabu search (Acid and de Campos, 2003; Bouckaert, 1995), branch and bound (Tian, 2000), genetic algorithms and evolutionary…”
On Inclusion-Driven Learning of Bayesian Networks (2003, methodology): “This argument has been further supported by several authors (Heckerman et al., 1995, Madigan et al., 1996) who cite the following advantages: 1.”
Learning Bayesian networks: approaches and issues (2011, methodology): “…works on learning structures was by Heckerman et al. (1995), who analysed scoring functions from a Bayesian perspective and tested their techniques using a greedy learning algorithm described by Chickering et al. (1996), that added, removed or reversed an arc from the current DAG at each step.”
Learning Equivalence Classes of Bayesian Network Structures (1996, methodology): “F or our experimental results, we used the algorithm derived by Chickering (1995b), which i s asymptotically optimal in the average case.”
Exact Bayesian Structure Discovery in Bayesian Networks (2004, background): “Unfortunately, finding a structure that maximizes the posterior probability, ˆ G ∈ arg max G p(G | x) , is known to be NP-hard in general (Chickering et al., 1995).”
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs (2019, methodology): “rithms such as dynamic programming [45] or shortest path approaches [46, 47] can only solve small-scale problems. We have to resort to heuristic methods such as local search, simulated annealing etc. [48]. All these approaches optimize the network structure in the discrete space. In this paper, we p”
Maximum Entropy Discrimination (1999, unknown): “[5]).”
Causal Inference in the Presence of Latent Variables and Selection Bias (1995, background): “(Cooper and Herskovits 1992, Heckerman et al. 1994, Chickering et al. 1995.)”