Wednesday, March 19, 2014

How Statisticians Could Help Find That Missing Plane

Two Royal Malyasian Navy vessels conduct a search and rescue operation for the missing Malaysia Airlines plane over the Straits of Malacca, Malaysia, Thursday, March 13, 2014.

What happened to Malaysia Airlines Flight 370, and where is it now?

Statistical tools can’t answer those questions any more definitively than Malaysian officials have. Yet they can help refine and focus the hunt for the plane and for a solution to the deepening mystery of its March 8 disappearance.

Bayesian statisticians are particularly helpful in a search operation. Their methods allow hunters to update their estimates of the probability of finding their target in any latitude-longitude combination — or even in three dimensions, accounting for depth in the water. Bayesians helped hunt U-boats in World War II, a U.S. submarine in the 1960s and an Air France jet in 2011.

There’s a fourth dimension to the current search: the cause of the disappearance. New developments, such as information about how the plane’s communication systems were shut off, have lowered the probability that the plane disappeared because of an accident and increased the likelihood of deliberate diversion. Which explanation is the current leader, in turn, affects the probability of finding the plane at any given location: A deliberate act has made spots farther from the takeoff point of Kuala Lumpur more likely.

Bayesian inference formalizes what will seem, to many unfamiliar with it, like common sense. Its founding principle is that most new situations can be assessed and assigned probabilities: How likely is this restaurant to be good? How likely is this cough to be a cold? How likely is Duke to win the NCAA title?

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