Variational inference engine for probabilistic graphical models

A computer-implemented method for a variational inference engine comprising:

• inputting a specification for a model having a plurality of observable variables and a plurality of unobservable variables, the specification including a functional form for a plurality of conditional distributions of the model and a structure for a graph of the model having nodes for each of the plurality of observable variables and the plurality of unobservable variables;
• initializing a distribution for the plurality of unobservable variables that approximates the true posterior distribution, the distribution having a plurality of distribution components corresponding to the plurality of unobservable variables and that is based on the functional form for the plurality of conditional distributions of the model;
• performing a free-form minimization over each distribution component in a distribution component-by-distribution component manner;
• repeating performance of the free-form minimization until a predetermined convergence criterion has been met;
• and,
• outputting at least the distribution that approximates the true posterior distribution of the model.

A machine-readable medium having instructions stored thereon for execution by a processor to perform a method for a variational inference engine comprising:

• inputting a specification for a model having a plurality of observable variables and a plurality of unobservable variables, the specification including a functional form for a plurality of conditional distributions of the model and a structure for a graph of the model having nodes for each of the plurality of observable variables and the plurality of unobservable variables;
• determining a distribution for the plurality of unobservable variables that approximates the true posterior distribution based on the structure for the graph of the model and the functional form for the plurality of conditional distributions of the model, using variational methods;
• and,
• outputting at least the distribution that approximates the true posterior distribution of the model.

A computer-readable medium having instructions stored thereon for execution by a processor to perform a method for a variational inference engine comprising:

• inputting a specification for a model having a plurality of observable variables and a plurality of unobservable variables, the specification including a functional form for a plurality of conditional distributions of the model and a structure for a graph of the model having nodes for each of the plurality of observable variables and the plurality of unobservable variables;
• initializing a distribution for the plurality of unobservable variables that approximates the true posterior distribution, the distribution having a plurality of distribution components corresponding to the plurality of unobservable variables, and a functional form that factorizes over the plurality of unobservable variables and that is based on the functional form for the plurality of conditional distributions of the model;
• performing a free-form minimization over each distribution component in a distribution component-by-distribution component manner;
• repeating performance of the free-form minimization until a predetermined convergence criterion has been met;
• and,
• outputting at least the distribution that approximates the true posterior distribution of the model.