Corpus ID: 16810012

Generalizing variable elimination in Bayesian networks

  title={Generalizing variable elimination in Bayesian networks},
  author={F. Cozman},
  • F. Cozman
  • Published 2000
  • Mathematics
  • This paper describes a generalized version of the variable elimination algorithm for Bayesian networks. Variable elimination computes the marginal probability for some specified set of variables in a network. The algorithm consists of a single pass through a list of data structures called buckets. The generalization presented here adds a second pass to the algorithm and produces the marginal probability density for every variable in the buckets. The algorithm and the presentation focus on… CONTINUE READING
    108 Citations

    Figures from this paper

    Compiling Bayesian Networks Using Variable Elimination
    • 119
    • PDF
    Belief updating in Bayesian networks by using a criterion of minimum time
    • S. F. Galán
    • Mathematics, Computer Science
    • Pattern Recognit. Lett.
    • 2008
    • 2
    Bayesian Network Inference Using Marginal Trees
    • 2
    • Highly Influenced
    • PDF
    Bucket-Tree Elimination for Automated Reasoning
    • 17
    • PDF
    Bayesian Networks: a Non-Frequentist Approach for Parametrization, and a more Accurate Structural Complexity Measure Bayesian Networks Learning
    • PDF


    Bucket elimination: A unifying framework for probabilistic inference
    • 533
    • PDF
    Identifying independence in bayesian networks
    • 462
    Efficient inference in Bayes networks as a combinatorial optimization problem
    • 150
    • PDF
    Axioms for probability and belief-function proagation
    • 636
    • PDF
    Exploiting Causal Independence in Bayesian Network Inference
    • 487
    • PDF
    Probability functions on complex pedigrees
    • Mathematics
    • 1978
    • 226
    Clustering Without (Thinking About) Triangulation
    • 24
    • PDF
    Dynamic Jointrees
    • 15
    • PDF