Estimating the Parameters of Mixed Bayesian Networks from Incomplete Data
نویسندگان
چکیده
Under complete data, there are closed-form maximum likelihood estimators for mixed Bayesian networks composed of discrete models [1], conditional Gaussian models [2] and conditional Gaussian regression models [2]. We describe an extension to Lauritzen’ expectation-maximisation (EM) algorithm [3], which estimates the parameters of discrete networks from incomplete data, to the more general case of mixed continuous and discrete variable networks. A simple mixed network that is easy to manipulate is the leaf node continuous Bayesian network (LNCBN). Fast algorithms for estimation and marginalisation of LNCBNs are described.
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