Accession Number : ADA623158


Title :   Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data


Descriptive Note : Conference paper preprint


Corporate Author : CALIFORNIA UNIV LOS ANGELES DEPT OF COMPUTER SCIENCE


Personal Author(s) : Broeck, Guy Van den ; Mohan, Karthika ; Choi, Arthur ; Darwiche, Adnan ; Pearl, Judea


Full Text : http://www.dtic.mil/get-tr-doc/pdf?AD=ADA623158


Report Date : Jul 2015


Pagination or Media Count : 11


Abstract : We propose a family of efficient algorithms for learning the parameters of a Bayesian network from incomplete data. Our approach is based on recent theoretical analyses of missing data problems, which utilize a graphical representation called the missingness graph. In the case of MCAR and MAR data, this graph need not be explicit, and yet we can still obtain closed form asymptotically consistent parameter estimates without the need for inference. When this missingness graph is explicated (based on background knowledge), even partially, we can obtain even more accurate estimates with less data. Empirically we illustrate how we can learn the parameters of large networks from large datasets which are beyond the scope of algorithms like EM (which require inference).


Descriptors :   *BAYES THEOREM , *LEARNING MACHINES , *STATISTICAL INFERENCE , ACCURACY , ALGORITHMS , CLASSIFICATION , GRAPHS , INFORMATION PROCESSING , KNOWLEDGE MANAGEMENT , MARKOV PROCESSES , MATHEMATICAL MODELS , NETWORK ARCHITECTURE , PROBABILITY DISTRIBUTION FUNCTIONS


Subject Categories : Statistics and Probability


Distribution Statement : APPROVED FOR PUBLIC RELEASE