| What you actually want in this context is some code that generates random deviates of probability distributions chosen randomly and a "guesser agent" that tries to guess which distribution was chosen. Then you can ask questions like, > given some condition on a distribution of distributions, when do we feel that a guesser is taking too long to make a choice? This is like a person who is taking to long to identify a color or a baby making a decision about what kind of food it wants and waiting for it to do so. For a certain interval, it makes sense, but after a point it becomes pathological. So for example if we have two distributions, > uniform distribution on the unit interval [0,1]; uniform distribution on the interval [1,2] then we get impatient with a guesser who takes longer than a single guess, since we know (with probability 1) that a single guess will do. Now, if we have two distributions that overlap, say the uniform distribution on [1,3] and [0,2], then we can quantify how long it will take before we know the choice with probability 1, but we can't say for sure how many observations will be required before any agent capable of processing positive feedback in a neural network can say for certain which one it is. As soon as an observation leaves the interval (1,2) the guesser can state the answer. Now, things can get more interesting when the distributions are arranged in a hierarchy, say the uniform distribution on finite disjoint unions of disjoint intervals (a,b) where a < b are two dyadic rationals with the same denominator when written in lowest terms. If a guesser is forced to guess early, before becoming certain of the result, then we can compare ways to guess by computing how often they get the right answer. Observations now give two types of information: certain distributions can be eliminated with complete confidence (because there exists a positive epsilon such that the probability of obtaining an observation in the epsilon ball is zero) while for the others, Bayes theorem can be used to update a distribution of distributions or several distributions of distributions that are used to drive a guessing algorithm. A guess is a statement of the form "all observations are taken from the uniform distribution on subset ___ of the unit interval". Example: take the distributions on the unit interval given by the probability density functions 2x and 2-2x. Given a sequence of observations, we can ask: what is the probability that the first distribution was chosen? The answers to these questions can be found in a book like Probability : Theory and Examples. |