Food or water borne?
Assign diarrhea source using statistics. Dan Gillis University of Guelph I present a new statistical method to classify spatially correlated data into distinct groups, while estimating the effect of covariates, using a Mixture model with multivariate conditionally autoregressive random effects. The method provides parameter estimates as good or better than traditional spatial methods, while at the same time classifying the data into distinct groups; an option unavailable to traditional spatial methods. The method was applied to Gastrointestinal data which were classified as either foodborne or waterborne in nature. See also this haiku. Multivariate
Spatial Poisson Mixtures Are My Bitch Forever Dan Gillis University of Guelph I present a new statistical method to classify spatially correlated data into distinct groups, while estimating the effect of covariates, using a Mixture model with multivariate conditionally autoregressive random effects. The method provides parameter estimates as good or better than traditional spatial methods, while at the same time classifying the data into distinct groups; an option unavailable to traditional spatial methods. The method was applied to Gastrointestinal data which were classified as either foodborne or waterborne in nature. dim star, far away,
do you have a companion? we can’t tell from here. Floyd Bullard Duke University There exist sophisticated statistical methods to determine whether radial velocity measures from a distant star are consistent with those one would observe from earth if the star were hosting an orbiting “exoplanet”. My dissertation was an attempt to find an even better method that would pick up on signals possibly missed today. (I found no such method.) Film of railroad track
Bayes helps to clean the blurring Defects detected! Tom Short Carnegie Mellon University I applied Bayesian methods to remove distortions and to detect defects more or less simultaneously in video images taken of the surface of railroad tracks. What’s the chance of rain?
Negative thirty percent! It’s not crazy talk. G. Jay Kerns Youngstown State University Dissertation Title: “Signed Measures in Exchangeability and Infinite Divisibility” |
Publisher/EditorJanine Allwright
Graduate Student Walden University Public Policy and Public Administration Archives
December 2016
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