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? 
 
 Our first estimate of these probabilities may be no better than an educated guess. For example, we know that 60 percent of our restaurant meals in town have been good, or that Duke has won titles in four of the last 25 seasons (16 percent). 
 
 Then we start layering new information. The restaurant is full. Now we can feel more confident in our choice: All of our good meals in town have been in full restaurants, but just half of our bad meals have been. What is the chance of a good meal, given that a restaurant is full? It’s 75 percent, based on this new information, since 75 percent of meals in full restaurants have been good. Before ordering, we check our favorite food-review website and see that the place has four and a half stars out of five. Every meal we’ve eaten at restaurants rated that highly has been good, but just half of our meals at restaurants with lower ratings have been. So we update our probability again, accounting for any overlap between full restaurants and highly rated ones — until we eat, when probability is no longer a relevant concept because our mouths are full. 
 
 Apply the same ideas to Duke, and you might examine the Blue Devils’ current ranking, their recent games, the probability FiveThirtyEight’s model and others assign to the team’s title hopes, and other tools to update that coarse, 16 percent probability. 
 
 These examples require calculation of a single probability. Targeting a search in an area requires a probability estimate for every point in that area, really a probability distribution. Initially, we might guess that the probability is uniform: The object of interest is equally likely to be at any point. Then we update that distribution based on new information, such as — in the case of a missing plane — flight path, wind, ocean flow and which areas have been searched already. 
 
 This approach has informed water searches for sunken treasure, for men overboard and for plane-crash debris, said Lawrence D. Stone, chief scientist at Metron Scientific Solutions, who has worked on many of these searches. Among them: the hunt for the remnants of Air France Flight 447, which crashed in 2009 en route from Rio de Janeiro to Paris, killing all 228 people on board. 
 
 Stone and his team’s methods helped inform a fifth Air France search two years after the crash, after four other efforts failed. Within six days, they found the wreck, and helped to show that the crash likely occurred because of pilot error in response to autopilot mode disengaging. After waiting nearly two years to understand their relatives’ disappearance, passengers’ families finally had answers. “We were very pleasantly surprised,” Stone said by email. “It doesn’t always happen this way.” 
 
 Among the challenges is finding basic information about which areas have already been searched. “Search managers typically assume the search will be resolved quickly; only when it drags on do they realize they should have employed better record-keeping from the start,” Colleen M. Keller, a Metron senior analyst who worked with Stone on the Air France hunt, said by email. “Without good records, it will be very difficult for us to reconstruct and credit the current search effort.” 
 
 Even with ample data, Bayesian hunters must quantify subjective judgment, “using expert testimonies and imagination,” as Nozer Singpurwalla, a professor of risk analysis and management science at the City University of Hong Kong, put it in an email. 
 
 The Metron team outlined its success in a paper that makes plain the subjectivity inherent to the approach. For instance, the team had to account for the possibility that an earlier search covered an area including the wreckage site but missed it. So they examined each prior search, one by one. 
 
 In the second search, in June and July 2009, two U.S. Navy ships listened for acoustic signals from beacons on the flight data recorder (also called the black box) and the cockpit voice recorder. Searchers designed the ships’ path to ensure that they got within 1,730 meters (a little over a mile) of every point in the area of the Atlantic they were scanning. Metron searchers had to calculate the probability that this search failed not because it was in the wrong area but because the beacons malfunctioned. This step alone, critical in determining how likely it was that a repeat search in that area would yield the wreckage, required several intuitive estimates, or educated guesses, if you like. 
 
 First, the scientists calculated the probability the beacon would be heard within 1,730 meters as at least 90 percent. Then they capped that probability at 90 percent, based on learning from past searches that “detection estimates based on manufacturers’ specifications and operator estimates tend to be optimistic.” 
 
 “I had been burned a couple of times before by optimistic sensor performance estimates,” Stone said. 
 
 Next they had to account for the possibility the beacons had been damaged in the crash. This, too, required extrapolating from past crashes, which yielded an 80 percent probability each beacon was unscathed. But how close together were the beacons — and if one beacon was damaged, was the other more likely to be? Or was the probability of either one being damaged independent? The scientists figured there was a 25 percent chance of independence and 75 percent chance of dependence. That, in turn, yielded a probability of 77 percent that if the wreck were in that area, the beacon search would have turned it up. And that was just one piece of one adjustment to the scientists’ estimate of where they might find the plane wreckage. 
 
 This method may sound like making up numbers, but to advocates of Bayesian techniques, applying a rigorous framework to expert subjective judgment is valuable. “This is one of the strengths of the Bayesian method,” Stone said. “We did not have a thousand examples of AF447-like crashes to guide us. This is when the Bayesian approach is most useful.” 
 
 There are far fewer than 1,000 examples of past missing airplanes to guide the current search — just 80 since 1948, according to the Aviation Safety Network. Only two planes in the last seven years disappeared for at least 10 days: Adam Air Flight 574, in 2007; and Air France Flight 447. That makes subjective judgment a crucial input into the search, whether it’s being done qualitatively, or quantitatively using Bayesian methods. 
 
 Keller said Metron isn’t involved in the Malaysia Airlines hunt. If it were, the same principles would apply: Start with all data, such as radar, visual or acoustic measurements, transmissions from the plane and so on. Then update to account for unsuccessful searches, and keep updating as new information comes in. “Bayesian search theory allows flexibility in this way and even accommodates conflicting information,” Keller said. “Nothing is discounted.” 
 
 The extra layers of complexity in the Malaysia Airlines search — the new estimates of the plane’s location, mounting evidence that a deliberate act caused the disappearance — complicate the Bayesian calculations and estimates. 
 
 Bradley Efron, a Stanford University statistician, said the complications make Bayes a bad fit for the Malaysia Airlines hunt. “Bayes’ Rule is good for refining reasonable (or at least not unreasonable) prior experience on the basis of new evidence,” Efron, who also expressed skepticism to Al Jazeera America, wrote in an email. “It is not good when new evidence changes the situation drastically.” 
 
 But Tony O’Hagan, professor emeritus of probability and statistics at the University of Sheffield in the U.K., said that’s the perfect situation for Bayesian techniques, which should make searchers most effective in adapting to changing information, so long as they properly assume from the get-go that the plane might not be in the initial search area. 
 
 Other advocates of Bayesian stats pointed to their usefulness in bringing discipline to what can be a difficult search process. Citing the work of Daniel Kahneman and Amos Tversky on decision-making, O’Hagan said, “People have two modes of thinking. There’s a quick, instinctive mode and a slow, thoughtful mode. When problems are important enough, we need to force ourselves into the second mode — and Bayesian methods are exactly what we need.” He added, “It is likely that Bayes’ theorem would adjust faster than people would tend to do using quick-mode thinking” — because people can get locked into their quick conclusions, while Bayes slowly and steadily tacks. 
 
 Arnold I. Barnett, a statistician at the MIT Sloan School of Management, worries that people who use the tools without fully understanding them may be led astray, “that the very act of quantifying a probability obscures the point that the numerical estimate is itself subject to uncertainty,” he said. “Thus the estimate might be taken more literally than is warranted.” 
 
 Despite the widespread use of Bayesian methods in searches, not every airline or government uses them — there’s no evidence the Malaysian government nor Malaysia Airlines has in the current search, though they are getting help from a team of French government investigators who worked on the Air France hunt. (The Malaysian embassy in Washington, D.C., didn’t immediately respond to an emailed request for comment, and calls to the embassy on Monday yielded an automated reply that the office was closed because of snow.) 
 
 “I suspect that they just guess, like professional baseball managers used to do before ‘Moneyball,’” said Peter Thall, a biostatistician at MD Anderson Cancer Center in Houston. 
 
 CORRECTION (March 18, 11:30 a.m.): An earlier version of this article incorrectly said Metron joined the search for Air France Flight 447 two years after the crash. Metron joined earlier; a search based on its Bayesian calculations that found the wreck began two years after the crash. 
 
 CLARIFICATION: An earlier version of this article garbled an example of Bayesian inference about choosing a restaurant. The example should have said that meals were good at half of restaurants with ratings below four and a half stars, not that half of lousy meals have been good. ||||| Image copyright AFP Image caption Members of the Brazilian Frigate Constituicao recovering debris in June 2009 
 
 Statisticians helped locate an Air France plane in 2011 which was missing for two years. Could mathematical techniques inspired by an 18th Century Presbyterian minister be used to locate the mysterious disappearance of Malaysia Airlines Flight MH370? 
 
 In June 2009, Air France flight 447 went missing flying from Rio de Janeiro in Brazil to Paris, France. 
 
 Debris from the Airbus A330 was found floating on the surface of the Atlantic five days later, but the mystery of why the plane crashed could only be answered by finding the black box and the cockpit voice recorder. 
 
 You may think that having found the debris it would be easy to find the rest of the plane, but it's not that simple - after a number of days, the material would have moved with the ocean current. 
 
 Software does exist that can simulate how the debris has travelled from the initial impact. It is used regularly by the US Coast Guard. 
 
 But in this case, because this area near the equator is known for unpredictable currents - particularly at that time of year - it was no help. 
 
 Image copyright Getty Images Image caption Debris from the Air France crash is laid out for investigation in 2009 
 
 American, Brazilian and French ships, planes and submarines all searched for the plane, but they couldn't find it. 
 
 At this point France's aviation accident investigation authority, BEA, made a call to a group of statisticians in the US who had expertise in finding objects lost at sea. 
 
 Senior analyst Colleen Keller flew to France to help. 
 
 More or Less: Behind the stats Listen to More or Less on BBC Radio 4 and the World Service, or download the free podcast Download the More or Less podcast More stories from More or Less 
 
 "The French BEA had already done a wonderful job of coming up with different theories for why the aircraft might have crashed," she says. 
 
 They also had lots of data about historical crashes and the results of the searches that had already been carried out. 
 
 To turn all this information into numbers and probability, Keller and her team from Metron Inc in Virginia, relied on Bayesian statistics named after a British Presbyterian minister called Thomas Bayes. 
 
 This type of thinking allows you to assess various scenarios at once - even contradictory ones. The probability of each being true is brought together to give you the most likely solution. And if you find new information, you can revise your model easily. 
 
 Keller and her colleagues went through all the available information and assessed the uncertainties of each piece of data - applying Bayesian principles of probability to work out the most likely location of the plane. 
 
 The team split up the search area into a grid, and applied to each cell a figure representing the probability that the plane would be found there. 
 
 Image copyright AFP/Getty Images Image caption A Brazilian Air Force radar plane prepares to leave Fernando de Noronha airport to search for Flight 447 
 
 To calculate these figures, they first looked at the theories about what caused the plane to crash. For instance, they assessed the likeliness of various mechanical failures, and came up with a probability for each scenario. 
 
 They then assessed historical data from previous crashes, noting, for example, that planes were usually found very close to where they were last known to have been. 
 
 Finally, Keller and her team lowered the probability of the plane being found in locations that had already been searched. 
 
 "There are two components to Bayesian maths which make it unique. It allows you to consider all the data you have including the uncertainties which is very important because nothing is certain," says Keller. 
 
 "And to combine it all - it even allows you to combine views that contradict each other. 
 
 "For instance with the Malaysian search, you have that arc to the north and the arc to the south. It's either one or the other but it can't have gone both ways, but [Bayes] allows you to preserve all your theories and weight them." 
 
 The second benefit is that the Bayesian approach is very flexible, Keller says. It allows you to update your body of knowledge at any time. If something new comes up, you factor it in and update the probability map. 
 
 Image copyright EUMETSAT HO Image caption A 2009 infrared satellite image shows weather conditions off the Brazilian coast and the plane search area 
 
 In the case of the Air France plane, they could be sure that the plane had come down within a 40-mile radius of the last location pinged out by its on-board computer system. 
 
 Who was Thomas Bayes? Image copyright Other Born in London, 1702, the eldest of seven children 
 
 Studied logic and theology at University of Edinburgh from 1719 until 1722 
 
 Became the The Reverend Thomas Bayes, serving as minister in a Presbyterian chapel 
 
 But as a Nonconformist, he did not follow Church of England doctrines or practices 
 
 Best known for his mathematical work on probability, giving rise to Bayes' Theorem 
 
 Bayesian probability estimates are used all over the world, built into software that forecasts events including financial markets and weather 
 
 Died in Royal Tunbridge Wells in 1761 
 
 Yet this area was so huge that the investigators were struggling to know where to look. 
 
 The probability map Keller provided gave, by contrast, a much tighter area to search. 
 
 A team went out there, hoping that finally the mystery would be solved. But those hopes were dashed. There was no sign of the plane. 
 
 It seemed the statisticians could not help after all. 
 
 Some months later, though, Air France got back in touch and asked Keller to make one last attempt to analyse the data. 
 
 This time, she and her colleagues decided they were not happy with one of their initial assumptions. 
 
 The historical data showed that after a crash, the black box will be emitting a signal in 90% of cases. 
 
 In the immediate aftermath of the crash, search teams had spent a lot of time sweeping the areas close to the last known location, listening for the ping of the black box or voice recorder. 
 
 They had heard nothing. So Keller and her team had decided there was a very low probability the plane would be found there. 
 
 But what if neither the black box nor the voice recorder were sending a signal? 
 
 Image copyright Getty Images Image caption Alain Bouillard of the BEA speaking about the Air France 447 black boxes, found in 2011 
 
 The Metron statisticians now adapted their model to this possible scenario and came up with a new area of highest probability. 
 
 A team returned to the scene to look - and this time they found the plane. 
 
 The mystery of the crash was solved. The black box and voice recorder data appear to show that the pilots were given faulty speed readings, responded inappropriately, and lost control of the plane. 
 
 It's very likely if we don't get any breakthroughs, [Malaysia Airlines flight MH370 is] at the bottom of the Indian Ocean and we will never find it, sadly Colleen Keller 
 
 "It still was a minor miracle that we found it," says Keller. 
 
 "It was lucky that the wreckage was on the bottom of the ocean floor, on a very sandy area. There were some areas down there that looked like the Himalayas - in terms of mountains, crags, and valleys." 
 
 If the plane had been in one of those areas, she says, "it could have been undetected forever". 
 
 Keller says she is not sure Malaysia Airlines Flight 370 will be found. 
 
 "It's a big world out there. And I know people are saying - how could you possibly hide or not find a Boeing 777? 
 
 "[But] it's very likely if we don't get any breakthroughs, it's at the bottom of the Indian Ocean and we will never find it, sadly." 
 
 Even finding debris might not mean finding the bulk of the plane. 
 
 "If we found wreckage at this point, it would tell us it was in one body of water rather than the other," Keller says. "But it's so long since the plane would have crashed that I don't think the wreckage is going to be very helpful." 
 
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– Forget satellite images and aerial searches—the best way to find Malaysia Airlines Flight MH370 may be with mathematical techniques dating back to the 18th century, the BBC reports. That's how Air France flight 447 was found in 2009, using "Bayesian statistics" to measure the probability of the plane being in one place or another. Named after Presbyterian minister and mathematician Thomas Bayes, the technique allowed experts to apply several factors to each point on a map: For example, what was the chance it crashed from mechanical failure? How far do planes tend to crash from their last known location? What was the chance that search teams missed debris in various locations? It's like picking a restaurant by balancing how full it is, what your favorite restaurant-review website says, and so on—except that experts hunting for Airbus A330 did that for each point where plane may have crashed in the Atlantic, Five Thirty-Eight notes. It was so hard that the US team of statisticians invited by France gave up, until they de-emphasized one statistic: that a plane's black box emits a signal after a crash 90% of the time. They changed their findings, and presto, the plane was found. Bayesian techniques have helped people find World War II U-boats, men overboard, and sunken treasure, but there's no evidence that Malaysia is employing them now. "I suspect that they just guess, like professional baseball managers used to do before Moneyball," says a biostatistician.