The theorem is used to analyze scientific results, and also to look for things. Metron Corp. has used Bayes' theorem to hunt for everything from sunken submarines to gold bullion.
Colleen Keller, a senior analyst with Metron, was called in to hunt for Air France Flight 447, which disappeared somewhere over the Atlantic Ocean in 2009. A sonar search for "pinger" beacons on the plane's two black boxes had turned up no sign of the wreckage. "[They] were kind of throwing up their hands," she says. "They brought us onboard to start collating the data and keep them organized."
Keller went to work developing a Bayesian model to look for the plane. She started with data on the flight's last known position, along with details of currents and weather, and stats from previous crashes. She calculated the odds of various scenarios and combined them into a huge probabilistic framework. The mathematical model calculated the odds of the plane being at any one point in a nearly 50-mile radius of the last point of contact. Keller updated those odds after searchers passed over a section of ocean.
But the theorem is only as good as the facts it includes. In the case of Air France, the failure of both black boxes — against the odds — kept the theorem from initially succeeding.
For the missing Malaysian Airlines flight, the problem may be the sea itself. The southern Indian Ocean is vast, deep and prone to bad weather and powerful currents. Search planes can miss things; boats can pass over wreckage and not even realize it.
"If the search itself is imperfect, then the revisions under Bayes' theorem will still leave a great deal of uncertainty," warns Barnett. "You might not be that far away from where you started."
Still, Keller says, Bayes' theorem probably does have something to offer. Even if the search area it predicts proves vast, "it's going to be better than searching the whole Indian Ocean."
- More Here
Colleen Keller, a senior analyst with Metron, was called in to hunt for Air France Flight 447, which disappeared somewhere over the Atlantic Ocean in 2009. A sonar search for "pinger" beacons on the plane's two black boxes had turned up no sign of the wreckage. "[They] were kind of throwing up their hands," she says. "They brought us onboard to start collating the data and keep them organized."
Keller went to work developing a Bayesian model to look for the plane. She started with data on the flight's last known position, along with details of currents and weather, and stats from previous crashes. She calculated the odds of various scenarios and combined them into a huge probabilistic framework. The mathematical model calculated the odds of the plane being at any one point in a nearly 50-mile radius of the last point of contact. Keller updated those odds after searchers passed over a section of ocean.
But the theorem is only as good as the facts it includes. In the case of Air France, the failure of both black boxes — against the odds — kept the theorem from initially succeeding.
For the missing Malaysian Airlines flight, the problem may be the sea itself. The southern Indian Ocean is vast, deep and prone to bad weather and powerful currents. Search planes can miss things; boats can pass over wreckage and not even realize it.
"If the search itself is imperfect, then the revisions under Bayes' theorem will still leave a great deal of uncertainty," warns Barnett. "You might not be that far away from where you started."
Still, Keller says, Bayes' theorem probably does have something to offer. Even if the search area it predicts proves vast, "it's going to be better than searching the whole Indian Ocean."
- More Here
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