Name Data lournal U.SS. Scorpion and Dr. John Craven Read this article Also read
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Name Data lournal U.SS. Scorpion and Dr. John Craven Read this article Also read; https://en.wikipedia.org/wiki/Bayesian, search the Watch this video httos/www.voutube com/watch veWh HWWk This is an example of how Statistics can be used in a practical application. Write a brief explanation of how Bayesian Search Theory was used to predict the likely location of the wreck. Compare the initial U.S. Navy search time to the search time with the help of Dr. Craven. Identify two concepts of probability that were used. What impresses you the most about this use of Statisics? Look up "Bayesian Search Theory" and cite at least two other cases where this application has helped find a missing object.Explanation / Answer
Air France Flight AF 447, with 228 passengers and crew aboard, the flight disappeared during bad weather over the Atlantic
four intensive searches had failed to find the aircraft. The wreckage was found almost exactly where they predicted at a depth of 14,000 feet after only one week’s additional search.
Stone and co explain how they did it. Their approach was a technique known as Bayesian inference. The result is a probability distribution for the location of the wreckage.
Bayesian inference is a statistical technique that mathematicians use to determine some underlying probability distribution based on an observed distribution.
In the case of Air France Flight 447, the main distribution was the probability of finding the wreckage at a given location. That depended on a number of factors such as the last GPS location transmitted by the plane, how far the aircraft might have traveled after that and also the location of dead bodies found on the surface once their rate of drift in the water had been taken into account.
Now stone and co. find it with statisticians call the posterior distribution. To calculate that, Stone and co. had to know about the failure of four different searches. The first was the failure to find debris or bodies for six days after the plane went missing in June 2009; then there was the failure of acoustic searches in July 2009 to detect the pings from underwater locator beacons on the flight data recorder and cockpit voice recorder; next, another search in August 2009 failed to find anything using side-scanning sonar; and finally, there was another unsuccessful search using side-scanning sonar in April and May 2010.
The key point was that Bayesian inference itself can’t solve these problems. Instead, statisticians themselves play a crucial role in evaluating the evidence, deciding what it means and then incorporating it in an appropriate way into the Bayesian model.
The end result was the discovery of the wreckage along with the flight data recorder and cockpit voice recorder, which provided vital evidence about the aircraft’s final moments. It also led to the discovery of many more bodies that were then reunited with grieving families.
The lesson from the search for Air France flight AF 447 is that Bayesian inference is a powerful tool in searches of this kind but that the way it is applied is crucial too. In other words, statisticians also play an important role in this.
two concepts that were used was :-
the probability distribution and posterior distribution.
statistics plays an important role in that case
the way the uses probability distribution and posterior distribution was impressive. and it all depends on a number of factors such as the last GPS location transmitted by the plane, how far the aircraft might have traveled after that and also the location of dead bodies found on the surface once their rate of drift in the water had been taken into account.
TWO OTHER CASES
Malaysia Airlines MH370 and USS SCROPIAN[5]
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