Generating data analysis programs from statistical models (position paper). (English) Zbl 1044.68527

Taha, Walid (ed.), Semantics, applications, and implementation of program generation. International workshop, SAIG 2000, Montrèal, Canada, September 20, 2000. Proceedings. Berlin: Springer (ISBN 3-540-41054-6). Lect. Notes Comput. Sci. 1924, 212-229 (2000).
Summary: Extracting information from data, often also called data analysis, is an important scientific task. Statistical approaches, which use methods from probability theory and numerical analysis, are well-founded but difficult to implement: the development of a statistical data analysis program for any given application is time-consuming and requires knowledge and experience in several areas. In this paper, we describe AUTOBAYES, a high-level generator system for 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 thus a fully declarative problem description, similar in spirit to a set of differential equations. From this model, AUTOBAYES generates optimized and fully commented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Code is generated by schema-guided deductive synthesis. A schema consists of a code template and applicability constraints which are checked against 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. In this paper, we outline the AUTOBAYES system, its theoretical foundations in Bayesian probability theory, and its application by means of a detailed example.
For the entire collection see [Zbl 0947.00043].


68N01 General topics in the theory of software
62-07 Data analysis (statistics) (MSC2010)
62-04 Software, source code, etc. for problems pertaining to statistics


Matlab; Octave
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