Based on possibly incomplete data, the score and the observed information are derived for these models. This accounts for both the tra ditional score and observed information, de rived as derivatives of the log-likelihood, and the posterior score and observed information, derived as derivatives of the log-posterior dis tribution. Throughout the paper the special ization into recursive graphical models is ac counted for by a simple example. tables, missing data, probabilistic expert sys tems, recursive graphical models, exponential models, gradient, Hessian, multivariate nor mal prior, Dirichlet prior. The recursive exponential models (REMs) of this pa per have evolved from the recursive graphical models
REF analyzes this class of models, which he calls recursive exponential models (REMs).
Score and Information for Recursive Exponential Models with Incomplete Data
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