Solving inverse problems with PyMC

Question

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                        # this value is unknown in the real application
x = rnormal(0, 0.01, size=10000) # initial guess

for i in range(5):
    X = Normal('X', x.mean(), 1./x.var())
    Y = X*X                        # f(x) = x*x
    OBS = Normal('OBS', Y, 0.1, value=xtrue*xtrue+rnormal(0,1), observed=True)
    model = Model([X,Y,OBS])
    mcmc = MCMC(model)
    mcmc.sample(10000)

    x = mcmc.trace('X')[:]       # posterior samples

The posterior is not converging to xtrue.

Answer 1

The functionality purposed by @user1572508 is now part of PyMC under the name stochastic_from_data() or Histogram(). The solution to this thread then becomes:

from pymc import *
import matplotlib.pyplot as plt

xtrue = 2 # unknown in the real application
prior = rnormal(0,1,10000) # initial guess is inaccurate
for i in range(5):
  x = stochastic_from_data('x', prior)
  y = x*x
  obs = Normal('obs', y, 0.1, xtrue*xtrue + rnormal(0,1), observed=True)

  model = Model([x,y,obs])
  mcmc = MCMC(model)
  mcmc.sample(10000)

  Matplot.plot(mcmc.trace('x'))
  plt.show()

  prior = mcmc.trace('x')[:]

Answer 2

The problem is that your function, $y = x^2$, is not one-to-one. Specifically, because you lose all information about the sign of X when you square it, there is no way to tell from your Y values whether you originally used 2 or -2 to generate the data. If you create a histogram of your trace for X after just the first iteration, you will see this:

This distribution has 2 modes, one at 2 (your true value) and one at -2. At the next iteration, x.mean() will be close to zero (averaging over the bimodal distribution), which is obviously not what you want.