论文信息 - A Formalism for Building Causal Polytree Structures Using Data Distributions

A Formalism for Building Causal Polytree Structures Using Data Distributions

In this paper we have considered the problem of approximating an underlying distribution by one derived from a dependence polytree. This paper proposes a formal and systematic algorithm, which traverses the undirected tree obtained by the Chow method [2], and by using the independence tests it successfully orients the polytree. Our algorithm uses an application of the Depth First Search (DFS) strategy to multiple causal basins. The algorithm has been formally proven and rigorously tested for synthetic and real-life data.

[1] C. N. Liu,et al. Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[2] Nir Friedman,et al. The Bayesian Structural EM Algorithm , 1998, UAI.

[3] B. John Oommen,et al. On using the chi-squared metric for determining stochastic dependence , 1992, Pattern Recognit..

[4] Alice M. Agogino,et al. Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information , 2013, UAI.

[5] Edward H. Herskovits,et al. Computer-based probabilistic-network construction , 1992 .

[6] Dan Geiger,et al. Learning Causal Trees from Dependence Information , 1990, AAAI.

[7] Sanjoy Dasgupta,et al. Learning Polytrees , 1999, UAI.

[8] R. A. Leibler,et al. On Information and Sufficiency , 1951 .

[9] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[10] Judea Pearl,et al. An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation , 1992, UAI.

[11] Nir Friedman,et al. Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm , 1999, UAI.

[12] Andrew P. Sage,et al. Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.