Bayesian methods for WiSAR
In which I discuss two-and-a-half approaches to crowdsourcing Search & Rescue, and invite you to try one -- namely, mine: http://bit.ly/1eBBMkq
In which I discuss two-and-a-half approaches to crowdsourcing Search & Rescue, and invite you to try one -- namely, mine. (SciCast)
One of the giants in Bayesian statistics passed away at his home earlier this week, coincidentally at the start of the O'Bayes 250 conference marking the 250th anniversary of the publication of Bayes' paper. Writeup in X'ian's 'Og.
The C4ISR Journal had a recent search theory article quoting me along with Larry Stone.
The C4ISR Journal had a recent search theory article quoting me along with Larry Stone. I'm quite honored, like the British company in Dirk Gently's Holistic Detective Agency:
...was the only British software company that could be mentioned in the same sentence as ... Microsoft.... The sentence would probably run along the lines of ‘...unlike ... Microsoft...’ but it was a start.
It's a good article, covering the undeniably exciting historical origins hunting U-boats, and looking at what may be a modern renaissance. I think the article stretches to connect search theory with Big Data, but the author does note that when the data is visual, and you have humans scanning it for objects, there is a connection. With planning, it could have been used to prioritize the Amazon Mechanical Turk search for Jim Gray. (The resolution of the actual images in that search was probably too low regardless, but the core idea was sound.)
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".
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". EURO Journal on Decision Processes, Special Issue on Risk Management: http://link.springer.com/article/10.1007/s40070-013-0001-x
Alas, it's behind a paywall and the printed edition isn't due until Autumn. Here's an excerpt from the abstract:
Continue reading "Structured Methods for Intelligence Analysis"
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 to see how they compare.
~Originally a very cool review published in the first half of 2010. The review led to phone calls and a very productive collaboration on MapScore and other work.
Partly reconstructed March 2012.
Syrotuck's main study is his 1976, with N=242. But he gives much more detail about distance travelled in his 1975 paper, breaking distance down every 0.2 miles. Unfortunately, he only reports probabilities, not numbers, and doesn't even report total N. We know he got more data between 1975 and 1976, but didn't know how much. Is the 1975 breakdown representative of the 1976 data? Unfortunately, no one has Syrotuck's original data. But we re-created it. (Spreadsheets available!)
(This is recovered from an old history sidebar for SORAL. The code was updated in 2008 for the AGMSAR package.)
Version 2.0 release scheduled for 28 Feb 2003.
Jan. 2003: The code is now fully redocumented using dOxygen!! The public interface can be seen here. The private interface is available to developers upon request and right now is available here. In both cases the Developer's Manual is available under the "Related Pages" link in the header. Thanks to André Oboler!
Continue reading "SARBayes Optimal Resource Allocation Library"
NOTE: This is OLD software. It was only a proof-of-concept. It was NEVER operational. Needless to say, it is completely without warranty, etc. Continue reading "Instructions for “Probability Mapper”"
