Link
Apparently this article made it to the top of the NYT "most emailed" list last night.  Not a lot new there, but always nice to see Bayes and search theory in the news.

The Lognormal Distance Model

Eric Cawi

A logical extension of the Distance Rings model is to fit a smooth function to the distribution of data found in ISRID. Examining the Euclidean Distance data for different categories, it was found that a lognormal curve roughly captured the shape of the data. The Log-Normal (LN) is a two parameter distribution which assumes that the logarithm of your data follows a normal distribution. The probability density function of the LN curve is given by, where are the mean and standard deviation of the logarithm of distance.

Child 4-6 lognormal plot

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Charles R. Twardy

September 13, 2014

Thanks very much to summer intern Jonathan Lee (@jonathanlee1) for many MapScore fixes.  Jonathan is a keen Python programmer with extra geek points for running Linux on his Macbook Air and having an ASCII-art avatar.  He learned his way around Django in no time and brought us a slew of features and code refactoring including: Continue reading

Bluefin-21 Analysis

One of our SciCast forecasters posted an excellent analysis of how he estimated the (remaining) chance of success for Bluefin-21 finding MH370 by the end of the question.

Forecast trend for Bluefin-21 success, on SciCast.

Forecast trend for Bluefin-21 success, on SciCast.

Jkominek was wondering why the probability kept jumping up, and created a Bayes Net to argue that there was no good estimation reason for it.  (There may be good market reasons -- cashing in to use your points elsewhere.)

Bayes net model created by jkominek to explore the Bluefin question.

Bayes net model created by jkominek to explore the Bluefin question.

 

The full blog post is here: http://blog.scicast.org/2014/06/11/scicast-bluefin-21-and-genie/

 

Incoherence & Mattson

The following figure is from a recent paper I co-authored*:

Figure from Karvetski et al. 2014 showing we get more accuracy by ignoring incoherent estimates than by simple unweighted averages.

Figure from Karvetski et al. 2014 showing we get more accuracy by ignoring incoherent estimates than by simple unweighted averages.  (The unfortunately abbreviated 'BS' means 'Brier Score'. Lower is better, with 0 being perfect.)

What implications does it have for making subjective "consensus" probability maps at the start of a search?

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RIP Dennis Lindley

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.

Berkshires 2006 Sweep Width Experiment

In the summer of 2006, Rick Toman (Massachusetts State Police) and Dan O'Connor (NewSAR) organized a sweep width experiment and summit called "Detection in the Berkshires" at Mount Greylock in Massachusetts.  In addition to the sweep-width experiment, Perkins & Roberts provided search tactics training for several teams, and the summit provided a chance for us to explore similarities and differences between formal search theory and formalized search tactics.  It was an important the chance to meet many key people, compare notes, and discuss ideas.  I wish I had been more diligent about following up. Many thanks to Rick & Dan for organizing the event, and to many others listed at the end. However, this post is mostly to provide a reference for the sweep width experiment.

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MapScore Updates

The MapScore site has been updated! The most exciting new feature is Batch Upload.

  • Batch Upload!  In one fell swoop, upload and re-score all your models and cases (or as many as necessary).  No more clicking around or messing with “active” vs “inactive” cases.  (Unless you want to...)
MapScore batch upload screen. New Nov. 2013.

MapScore batch upload screen. New Nov. 2013.

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