Solving inverse problems with PyMC
问题 Suppose we're given a prior on X (e.g. X ~ Gaussian) and a forward operator y = f(x) . Suppose further we have observed y by means of an experiment and that this experiment can be repeated indefinitely. The output Y is assumed to be Gaussian (Y ~ Gaussian) or noise-free (Y ~ Delta(observation)). How to consistently update our subjective degree of knowledge about X given the observations? I've tried the following model with PyMC, but it seems I'm missing something: from pymc import * xtrue = 2