Omega-Stat: An Environment for Implementing Intelligent Modeling Strategies

E. James Harner, Hanga C. Galfalvy
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:252-258, 1995.

Abstract

Omega-Stat is a new statistical environment built on Lisp-Stat, an object-oriented statistical programming environment. It contains extensible, reusable-component libraries for performing data management, multivariate analyses, modeling, and dynamic graphics. The point-and-click user interface allows instant access to all objects, including analysis and graphics objects comprising a semantic map. Knowledge, and methods for accessing this knowledge, are embedded within model objects and edge objects linking these models. This will allow the modeling process to be studied by following the analysis trails of expert analysts. The objective is to provide an "expert consultant" that is accessible as part of the man/machine interaction. Modeling strategies can then be built into Omega-Stat by using prior knowledge and data-analytic heuristics to guide the process of constructing the model tree and the iterative search for an "optimal" model.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-harner95a, title = {Omega-Stat: An Environment for Implementing Intelligent Modeling Strategies}, author = {Harner, E. James and Galfalvy, Hanga C.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {252--258}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/harner95a/harner95a.pdf}, url = {https://proceedings.mlr.press/r0/harner95a.html}, abstract = {Omega-Stat is a new statistical environment built on Lisp-Stat, an object-oriented statistical programming environment. It contains extensible, reusable-component libraries for performing data management, multivariate analyses, modeling, and dynamic graphics. The point-and-click user interface allows instant access to all objects, including analysis and graphics objects comprising a semantic map. Knowledge, and methods for accessing this knowledge, are embedded within model objects and edge objects linking these models. This will allow the modeling process to be studied by following the analysis trails of expert analysts. The objective is to provide an "expert consultant" that is accessible as part of the man/machine interaction. Modeling strategies can then be built into Omega-Stat by using prior knowledge and data-analytic heuristics to guide the process of constructing the model tree and the iterative search for an "optimal" model.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Omega-Stat: An Environment for Implementing Intelligent Modeling Strategies %A E. James Harner %A Hanga C. Galfalvy %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-harner95a %I PMLR %P 252--258 %U https://proceedings.mlr.press/r0/harner95a.html %V R0 %X Omega-Stat is a new statistical environment built on Lisp-Stat, an object-oriented statistical programming environment. It contains extensible, reusable-component libraries for performing data management, multivariate analyses, modeling, and dynamic graphics. The point-and-click user interface allows instant access to all objects, including analysis and graphics objects comprising a semantic map. Knowledge, and methods for accessing this knowledge, are embedded within model objects and edge objects linking these models. This will allow the modeling process to be studied by following the analysis trails of expert analysts. The objective is to provide an "expert consultant" that is accessible as part of the man/machine interaction. Modeling strategies can then be built into Omega-Stat by using prior knowledge and data-analytic heuristics to guide the process of constructing the model tree and the iterative search for an "optimal" model. %Z Reissued by PMLR on 01 May 2022.
APA
Harner, E.J. & Galfalvy, H.C.. (1995). Omega-Stat: An Environment for Implementing Intelligent Modeling Strategies. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:252-258 Available from https://proceedings.mlr.press/r0/harner95a.html. Reissued by PMLR on 01 May 2022.

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