Bayesian search theory
Bayesian search theory is the application of Bayesian statistics to the search for lost objects. It has been used several times to find lost sea vessels, for example the USS Scorpion. The usual procedure is as follows: - Formulate a number of hypotheses about what happened to the vessels.
- Corresponding to each hypothesis construct a probability distribution for the location of the vessel
- Construct a probability distribution for actually finding an object in location X if it really is in location X. In an ocean search, this is usually a function of water depth - in shallow water your chances of finding an object are good if you are looking in the right place. In deep water your chances are reduced.
- Combine the above information coherently to produce an overall probability distribution. This gives the probability of finding the vessel by looking in location X, for all possible locations X. (This is like a contour map of probability.)
- Construct a search path which starts at the point of highest probability and 'scans' over high probability areas, then intermediate probabilities, then the low probability areas.
- Revise all the probabilities continuously as you search, i.e. if you have searched location X then the probability that the vessel is there is greatly reduced and the probabilities of all other locations must be increased.
The advantages of the Bayesian method are that all information available is used coherently (i.e. in a leakproof manner) and the method automatically produces estimates of the cost, for a given success probability. That is, even before one starts searching, one can say "there is a 65% chance of finding it in a 5-day search. That will rise to 90% after 10 days and 97% after 15 days" or some such statement. Thus the viability of the search can be estimated.
See also Bayesian inference#Search theory
References Stone, Lawrence D., The Theory of Optimal Search, published by the Operations Research Society of America, 1975 De Groot, Morris H., Optimal Statistical Decisions, Wiley Classics Library, 2004.
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