B. J. Borghetti. Inference algorithm performance and selection under contrained resources. Master's thesis, 1996. Home/Search Document Details and Download
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A Survey of Algorithms for Real-Time Bayesian Network Inference - Guo, Hsu (2002) (2 citations) (Correct)
....predicting algorithm performance could provide the basis for more optimal metareasoner that guide the selection and execution of BN inference algorithms. Borghetti developed an empirical method for using performance data gathered offline to predict future performance of algorithms on new problems [Bo96]. A knowledge base is analyzed into a set of characteristics. Each algorithm is executed individually on a single knowledge base and provides a set of solutions. These solutions are analyzed and the performance profiles for each algorithm are generated. A mapping relation is created between the ....
....algorithm, and we need to run different algorithms for different problems to maximize run time performance. Therefore, it is necessary to systematically study the matching relationships between the spaces of problems and the spaces of algorithms. This line of research has been explored in [NJ96, Bo96, Wi97], but we believe that there are much more work could be done in this direction. In real time BN inference research, we can see that the majority of algorithms for real time Bayesian networks inference are anytime algorithms. Less work has been done on the multiple methods approach, probably ....
B. J. Borghetti, Inference algorithm performance and selection under contrained resources, MS Thesis, AFIT/GCS/ENG/96D-05, 1996.
Algorithm Selection for Sorting and Probabilistic Inference: A.. - Guo (2003) (Correct)
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B. J. Borghetti. Inference algorithm performance and selection under contrained resources. Master's thesis, 1996.
A Bayesian System for Integration of Algorithms for Real-Time.. - Guo (2002) (Correct)
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B. J. Borghetti, Inference Algorithm Performance and Selection Under Contrained Resources, MS Thesis, AFIT/GCS/ENG/96D-05, 1996.
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