首页|Findings from Brigham Young University Broaden Understanding of Machine Learning (Integrating Machine Learning and Bayesian Nonparametrics for Flexible Modeling of Point Pattern Data)
Findings from Brigham Young University Broaden Understanding of Machine Learning (Integrating Machine Learning and Bayesian Nonparametrics for Flexible Modeling of Point Pattern Data)
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NETL
NSTL
Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news originating from Provo, Utah, by NewsRx correspondents, research stated, “Two common approaches to analyze point pattern (location-only) data are mixture models and log-Gaussian Cox processes. The former provides a flexible model for the intensity surface at the expense of no covariate effect estimates while the latter estimates covariate effects at the expense of computation.” Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from Brigham Young University, “A bridge is built between these two methods that leverages the strengths of both approaches. Namely, Bayesian nonparametrics are first used to flexibly model the intensity surface. The posterior draws of the fitted intensity surface are then transformed into the equivalent representation under the log-Gaussian Cox process approach. Using principles of machine learning, estimates of covariate effects are obtained.” According to the news editors, the research concluded: “The proposed two-step approach results in accurate estimates of parameters, with proper uncertainty quantification, which is illustrated with real and simulated examples.(.” This research has been peer-reviewed.
ProvoUtahUnited StatesNorth and Central AmericaCy- borgsEmerging TechnologiesMachine LearningBrigham Young University
NETL
NSTL
Elsevier
