John W. Sheppard, Ph.D.

Affiliations: 
1997 Computer Science Johns Hopkins University, Baltimore, MD 
Area:
Machine learning, probabilistic graphical models, evolutionary algorithms, prognostics and health management
Website:
http://www.cs.montana.edu/sheppard
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Publications

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Dickson DW, Baker MC, Jackson JL, et al. (2019) Extensive transcriptomic study emphasizes importance of vesicular transport in C9orf72 expansion carriers. Acta Neuropathologica Communications. 7: 150
Scherrer B, Sheppard J, Jha P, et al. (2019) Hyperspectral Imaging and Neural Networks to Classify Herbicide-Resistant Weeds Journal of Applied Remote Sensing. 13
Perreault L, Sheppard J. (2019) Compact Structures for Continuous Time Bayesian Networks International Journal of Approximate Reasoning. 109: 19-41
Dehghanpour K, Nehrir HM, Sheppard JW, et al. (2018) Agent-Based Modeling of Retail Electrical Energy Markets with Demand Response Ieee Transactions On Smart Grid. 9
Strasser S, Sheppard J, Fortier N, et al. (2017) Factored Evolutionary Algorithms,” IEEE Transactions on Evolutionary Computation Ieee Transactions On Evolutionary Computation. 21: 281-293
Sturlaugson L, Perreault L, Sheppard J. (2017) Factored Performance Functions and Decision Making in Continuous Time Bayesian Networks Journal of Applied Logic. 22: 28-45
Perreault L, Thornton M, Sheppard J, et al. (2017) Disjunctive Interaction in Continuous Time Bayesian Networks International Journal of Approximate Reasoning. 90: 253-271
Dehghanpour K, Nehrir MH, Sheppard JW, et al. (2016) Agent-Based Decision Making in Electrical Energy Markets Using Dynamic Bayesian Networks and Sparse Bayesian Learning Ieee Transactions On Power Systems. 31: 4744-4754
Sturlaugson L, Sheppard JW. (2016) Uncertain Evidence in Continuous Time Bayesian Networks International Journal of Approximate Reasoning. 70: 99-122
Sturlaugson L, Sheppard JW. (2015) Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models Journal of Uncertainty Quantification. 3: 346-369
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