1. A system that facilitates statistical modeling in an artificial intelligence application, comprising a processor; a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions configured to implement the system, including: a gradient determination component that utilizes a data set for a set of variables and probabilistic inference to determine parameter gradients for a log-likelihood of a conditional Gaussian (CG) graphical model over those variables with at least one continuous variable and with incomplete observation data for at least one of the variables, the CG graphical model employed to deduce a cause of a given outcome represented by the data set; wherein the parameter gradients comprise conditional multinomial local gradients and at least one conditional Gaussian local gradient, the gradient determination component determines the conditional multinomial local gradients by performing a line search to update the parameters of an exponential model representation, converting the updated parameterization to a non-exponential representation, and utilizing a propagation scheme on the non-exponential representation to compute the next gradient.
1. A system that facilitates statistical modeling in an artificial intelligence application, comprising a processor; a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions configured to implement the system, including: a gradient determination component that utilizes a data set for a set of variables and probabilistic inference to determine parameter gradients for a log-likelihood of a conditional Gaussian (CG) graphical model over those variables with at least one continuous variable and with incomplete observation data for at least one of the variables, the CG graphical model employed to deduce a cause of a given outcome represented by the data set; wherein the parameter gradients comprise conditional multinomial local gradients and at least one conditional Gaussian local gradient, the gradient determination component determines the conditional multinomial local gradients by performing a line search to update the parameters of an exponential model representation, converting the updated parameterization to a non-exponential representation, and utilizing a propagation scheme on the non-exponential representation to compute the next gradient. 4. The system of claim 1 , the gradient determination component utilizes a Recursive Exponential Mixed Model (REMM) representation of the CG graphical model to facilitate in determining the parameter gradients, the Recursive Exponential Mixed Model has both continuous and discrete variables and is defined in terms of local models represented as regular exponential models that satisfy global and partial local variation independence.