0, \n\nwhere ISE(O) is independent of t. Thus, the ISE between the two distributions is \nguaranteed to decrease during learning, when the sample size goes to infinity. \n\nProof: Expected Generative Solution is ML Solution \n\nIn the case of a generative model which has no constraints (i.e., can model any \ndistribution), the maximum likelihood solution will have distribution px(a) = \n.Jy Ef':18(yi - a), i.e., the model will produce only the observations and all of \nthem with equal probability. For this case, we show that our sample-based method \nwill yield the same solution in expectation as ML. \n\nThe sample-based method converges to a local minimum of the energy, where \n(Va