Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
KIDS: A Semiautomatic Program Development System
IEEE Transactions on Software Engineering
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
Reuse technologies and their niches
Proceedings of the 21st international conference on Software engineering
Learning in graphical models
Towards automated synthesis of data mining programs
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Octave: A Free, High-Level Language for Mathematics
Linux Journal
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
Automated Software Engineering
Deductive Composition of Astronomical Software from Subroutine Libraries
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
Planware ¾ Domain-Specific Synthesis of High-Performance Schedulers
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Operations for learning with graphical models
Journal of Artificial Intelligence Research
FME '02 Proceedings of the International Symposium of Formal Methods Europe on Formal Methods - Getting IT Right
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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.