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A Bayesian scan statistic for spatial cluster detection

Document Type: Paper
Tags: Biosurveillance, Spatial Scan, Spatial Statistics

This paper develops a new Bayesian method for cluster detection, the ?Bayesian spatial scan statistic,? and compares this method to the standard (frequentist) scan statistic approach on the task of prospective disease surveillance.

A Bayesian spatial scan statistic

Document Type: Paper
Tags: Biosurveillance, Spatial Scan, Spatial Statistics

We propose a new Bayesian method for spatial cluster detection, the ?Bayesian spatial scan statistic,? and compare this method to the standard (frequentist) scan statistic approach. We demonstrate that the Bayesian statistic has several advantages over the frequentist approach, including increa...

A Fast Multi-Resolution Method for Detection of Significant Spatial Overdensities

Document Type: Paper
Tags: Biosurveillance, Statistical Data Mining for Astrophysics, Clustering, Efficient Statistical Algorithms, Kd-trees and Ball-trees, Applications, Spatial Statistics

Given an NxN grid of squares, where each square s_ij has count c_ij and an underlying population p_ij, our goal is to find the square region S with the highest density, and to calculate the significance of this region by Monte Carlo testing. Any density measure D, which depends on the total coun...

Andrew Moore

Document Type: Person
Tags: Link Analysis, Auton Fast Classifiers, Statistical Data Mining for Astrophysics, Cached Sufficient Statistics, Efficient Statistical Algorithms, Spatial Statistics, Life Science Data Mining, Logistic Regression, Locally Weighted Learning, GDA, AD-trees, Bayesian Networks, Kernel Density Estimation, Kd-trees and Ball-trees, Mixture Models, WSARE, Reinforcement Learning, Active Learning, Markov Decision Processes, K Nearest Neighbor, Astrostatistics, Clustering, Memory-based Learning, Biosurveillance, Applications, Optimization, Association Rules

Andrew began his career writing video-games for an obscure British personal computer. He rapidly became a thousandaire and retired to academia, where he received a PhD from the University of Cambridge in 1991. He researched robot learning as a Post-doc working with Chris Atkeson, and then moved ...

Cuevas CFF Clustering

Document Type: Software
Tags: Biosurveillance, Spatial Statistics

Weng-Keen Wong Alexander Gray Andrew Moore

Daniel Neill

Document Type: Person
Tags: Biosurveillance, Link Analysis, Clustering, Efficient Statistical Algorithms, Kd-trees and Ball-trees, Spatial Statistics

My main research interests are statistical machine learning and game theory. I am currently working on fast methods for detecting spatial overdensities (for example, clusters of disease cases).

Detecting Anomalous Patterns in Pharmacy Retail Data

Document Type: Paper
Tags: Spatial Statistics, Biosurveillance

in this workshop, we present our biosurveillance system that is used to collect feedback data from public health officials monitoring spatial scan clusters in nationwide over-the-counter pharmacy sales.

Jeff Schneider

Document Type: Person
Tags: Markov Decision Processes, Link Analysis, Statistical Data Mining for Astrophysics, Astrostatistics, Cached Sufficient Statistics, Memory-based Learning, Efficient Statistical Algorithms, Spatial Statistics, Locally Weighted Learning, GDA, Biosurveillance, Bayesian Networks, Kd-trees and Ball-trees, Applications, Optimization, WSARE, Active Learning

Dr. Jeff Schneider is an associate research professor in the Carnegie Mellon University School of Computer Science. He received his PhD in Computer Science from the University of Rochester in 1995. He has over 15 years experience developing, publishing, and applying machine learning algorithms...

N-Body Problems in Statistical Learning

Document Type: Paper
Tags: Statistical Data Mining for Astrophysics, Cached Sufficient Statistics, Astrostatistics, Efficient Statistical Algorithms, Memory-based Learning, Kd-trees and Ball-trees, Spatial Statistics

We present efficient algorithms for all-point-pairs problems, or 'N-body'-like problems, which are ubiquitous in statistical learning. We focus on six examples, including nearest-neighbor classification, kernel density estimation, outlier detection, and the two-point correlation. These include a...

Repairing Faulty Mixture Models using Density Estimation

Document Type: Paper
Tags: Statistical Data Mining for Astrophysics, Cached Sufficient Statistics, Astrostatistics, Clustering, Efficient Statistical Algorithms, Kd-trees and Ball-trees, Mixture Models, Spatial Statistics

Previous work in mixture model clustering has focused primarily on the issue of model selection. Model scoring functions (including penalized likelihood and Bayesian approxi- mations) can guide a search of the model pa- rameter and structure space. Relatively lit- tle research has addressed the ...