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AutoBayes: A System for Generating Data Analysis Programs from Statistical Models

AutoBayes: A System for Generating Data Analysis Programs from Statistical Models
AutoBayes: A System for Generating Data Analysis Programs from Statistical Models
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 use methods from probability theory and numerical analysis, are well-founded but dificult to implement: the development of a statistical data analysis program for any given application is time-consuming and requires substantial knowledge and experience in several areas. In this paper, we describe 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 a 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 environments. Code is produced by a schema-guided deductive synthesis process. 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. It is well-suited for tasks like estimating best-fitting model parameters for the given data. Here, we describe AutoBayes's system architecture, in particular the schema-guided synthesis kernel. Its capabilities are illustrated by a number of advanced textbook examples and benchmarks.
0956-7968
483-508
Fischer, Bernd
0c9575e6-d099-47f1-b3a2-2dbc93c53d18
Schumann, Johann
03135c8b-0f6e-4a20-9453-6a4e2d8a1e23
Fischer, Bernd
0c9575e6-d099-47f1-b3a2-2dbc93c53d18
Schumann, Johann
03135c8b-0f6e-4a20-9453-6a4e2d8a1e23

Fischer, Bernd and Schumann, Johann (2003) AutoBayes: A System for Generating Data Analysis Programs from Statistical Models Journal of Functional Programming, 13, (3), pp. 483-508.

Record type: Article

Abstract

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 use methods from probability theory and numerical analysis, are well-founded but dificult to implement: the development of a statistical data analysis program for any given application is time-consuming and requires substantial knowledge and experience in several areas. In this paper, we describe 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 a 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 environments. Code is produced by a schema-guided deductive synthesis process. 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. It is well-suited for tasks like estimating best-fitting model parameters for the given data. Here, we describe AutoBayes's system architecture, in particular the schema-guided synthesis kernel. Its capabilities are illustrated by a number of advanced textbook examples and benchmarks.

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Published date: May 2003
Organisations: Electronic & Software Systems

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Local EPrints ID: 262356
URI: http://eprints.soton.ac.uk/id/eprint/262356
ISSN: 0956-7968
PURE UUID: f9dd6fd8-c607-4cf5-9bcc-e831ad8181bc

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Date deposited: 12 Apr 2006
Last modified: 18 Jul 2017 08:52

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Contributors

Author: Bernd Fischer
Author: Johann Schumann

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