Learning Bayesian networks

By Richard E. Neapolitan. 2007

2,836 citations recorded by the source corpus.

Preface. I. BASICS. 1. Introduction to Bayesian Networks. 2. More DAG/Probability Relationships. II. INFERENCE. 3. Inference: Discrete Variables. 4. More Inference Algorithms. 5. Influence Diagrams. III. LEARNING. 6. Parameter Learning: Binary Variables. 7. More Parameter Learning. 8. Bayesian Structure Learning. 9. Approximate Bayesian Structure Learning. 10. Constraint-Based Learning. 11. More Structure Learning. IV. APPICATIONS. 12. Applications. Bibliography. Index.

Subfields: Artificial Intelligence

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Citations in the corpus

Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm (2005, background): “Introduction Graphical models are a popular probabilistic tool to analyze and visualize conditional independence relationships between random variables (see Edwards, 2000; Lauritzen, 1996; Neapolitan, 2004).”
Discovering causal interactions using Bayesian network scoring and information gain (2016, methodology): “Resultantly, heuristic search algorithms have been developed [16].”
Causal Feature Selection (2007, methodology): “The interested reader is therefore encouraged to pursue his reading, starting perhaps with [11, 28, 32, 25 ].”
A review of recent advances in learner and skill modeling in intelligent learning environments (2012, background): “The second is validation with external measures, such as post-tests of knowledge.”
Impact of Population Growth on the Water Quality of Natural Water Bodies (2017, background): “It measures the quality of the network based on data by computing the log-likelihood of the resultant network while learning the network structure by searching possible sets of edges among each node and computing the conditional probabilities [27].”
Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks (2006, methodology): “When no knowledge is available they are estimated using Nijk 1⁄4 N/ðriqiÞ (Heckerman et al. (1995)) with N the equivalent sample size, ri the number of states of variable i and qi the number of instantiations of the parents of variable i.”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “One of the most common methods to model and induce causal relations is by learning causal Bayesian networks (Neapolitan, 2004; Spirtes et al., 2000; Pearl, 2000).”
Structure Learning in Graphical Modeling (2016, background): “Textbooks on this problem include Spirtes et al. (2000), Neapolitan (2004), and Koller & Friedman (2009).”