TagsA.A. Milne ACH ArcGIS archaeology Australia Banning Bayesian BN BYU Chiacchia conferences Data Analysis Detection Doherty drone Golding Goodrich Hope humor Koopman Lin Lost Person Behavior LRC management MapSAR MapScore math motion NATSAR PM Probability Maps RCMP rescue Sava Search Theory simulation software SORAL Sweep Width Syrotuck testing VicPol video Washburn watershed model
Category Archives: Search Theory
My colleagues just published a paper in Euro Journal on Decision Processes, for their special issue on risk management.
Karvetski, C.W, Olson, K.C., Gantz, D.T., Cross, G.A., "Structuring and analyzing competing hypotheses with Bayesian networks for intelligence analysis". Continue reading
Thanks to Mike Goodrich for this RCMP Drone story. In short, the RCMP spotted a stranded driver by using a drone-mounted infrared camera. We hope the FAA soon settles on good regulations for such drone use in the US.
OSARA conference link, with highlights from Ken Chiacchia's talk. Continue reading
When searching for an image for this post, I came across several works by E.B. Banning applying search theory to archaeology: Sweep widths and the detection of artifacts in archaeological survey. (2011) [Science Direct] Detection functions for archaeological survey (2006). … Continue reading
In the previous post, we began to build a theory of detection over time as the result of a very large number of independent glimpses. By assuming the environment to be fixed for awhile, we moved all the environmental factors … Continue reading
We begin a four-part gentle introduction to search theory. Our topic is visual detection of targets by land searchers. Today we summarize Koopman Chapter 3, constructing the useful "inverse cube" detection model by starting from instantaneous glimpses with tiny detection … Continue reading
Don Ferguson just sent me an update on the MapSAR project -- he's presenting at a project meeting this week in the Grand Canyon. I'm blown away by his slides. They've got it: a GIS enabled search planning tool with … Continue reading
Lin & Goodrich at Brigham Young are working on Bayesian motion models for generating probability maps. They have an interesting model, but need GPS tracks to train it. It's a nice complement to our approach, and it will be interesting … Continue reading
The Correlation Why Care? Why not just believe the data? What is the AMDR? Teaser Appendix