Bayesian search theory rulz

Bayesian search theory uses the work of mathematician Thomas Bayes to find lost objects--particularly objects lost at sea. For example, submarines.What's great about this method is that it works with hunches. In the search for the USS Scorpion, John Craven used Bayesian search theory, along with Vegas-style rounds of betting by a group of experienced submariners, to construct a theory about where the Scorpion could be found. The key elements of Bayesian search theory are:

1. Creating a variety of hypotheses, and probabilities, about where the object might be
2. Determining the likelihood of finding the object in each of those places, assuming it's there (i.e., if the water's deeper, it's harder to find)
3. Multiplying these two together to make a probability map
4. Continuously revising the map as the search is conducted (i.e. when it's not found in the first location, it's more likely to be found in the second location, and so forth)

What does this have to do with anything, you ask? (But...you may ask that about nearly any post on this blog...) First, this technique is used in prediction markets and spam filtering online today. But second, I think there's no reason that the concept can't be used in just about any type of research where we're looking for something within a finite amount of area (physical or conceptual). In that case, the keys would be:

1. Defining the area in which you're searching
2. Developing good hunches
3. Quantifying the strength of those hunches
4. Knowing your subject matter well enough to determine the feasibility of the hunches
5. Doing the math

I'm going to try this the next time I'm looking for predictive analytics relationships!