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AutoBayes: Synthesis of Data Analysis Software

Data analysis is an important scientific task which is required whenever information needs to be extracted from raw data. Statistical approaches to data analysis , which uses methods from probability theory and numerical anaylsis, are well-foudned but difficult to implrement: the development of a statistical data analysis program for any given application is time-consuming and requires substantial knowledge and experience in several areas.

This project, AUTOBAYES, a program synthesis system for the generation of data analysis programs from statistical models. A statistical model specifies the properties for each problem variable (i.e., observation or parameter) and its dependencies in the form of a probability distribution. It is fully declarative problem description, similar in spirit to a set of differential equations. From such a model, AUTOBAYES generates optimized and fully commented C/C++ code which can be linked dynamically into the Matlab and Octave enviroments. Code is produced by a schema-guided deductive synthesis process. A schema consists of a code template and applicability constraints which are checked againts the model during synthesis using theorem proving technology. AUTOBAYES augments schema-guided synthesis by symbolic -algebraic computation and can thus derive closed-form solutions for many problems. It is well-suited for tasks like estimating best-fitting model parameters for the given data.

Reviewed 2012


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