Learning Continuous Distributions: Simulations With Field Theoretic Priors
Authors: Ilya Nemenman, William Bialek
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Learning of a smooth but nonparametric probability density can  be reg(cid:173) ularized using methods of Quantum Field Theory. We implement a field  theoretic  prior numerically,  test its  efficacy,  and  show  that  the  free  pa(cid:173) rameter of the  theory  (,smoothness scale') can  be determined self con(cid:173) sistently by the data; this forms an infinite dimensional generalization of  the  MDL principle.  Finally,  we  study  the implications of one's choice  of the prior and the parameterization and conclude that the  smoothness  scale  determination  makes  density  estimation  very  weakly  sensitive  to  the choice of the prior, and that even wrong choices can be advantageous  for small data sets. 
One of the central problems in  learning is  to  balance  'goodness of fit'  criteria against the  complexity of models.  An  important development in the Bayesian approach was  thus  the  realization  that there does  not need to  be  any  extra penalty for  model complexity:  if we  compute the total probability that data are generated by a model, there is a factor from the  volume in  parameter space-the 'Occam factor' -that discriminates  against models  with  more parameters  [1,  2].  This works remarkably welJ  for  systems with a finite  number of  parameters and creates a complexity  'razor'  (after  'Occam's razor') that is  almost equiv(cid:173) alent to  the  celebrated Minimal Description  Length (MDL)  principle  [3].  In addition,  if  the a priori distributions involved are strictly Gaussian, the ideas have also been proven to  apply to some infinite-dimensional (nonparametric) problems [4].  It is not clear, however,  what happens  if we  leave  the  finite  dimensional  setting  to  consider nonparametric prob(cid:173) lems  which  are  not Gaussian,  such  as  the  estimation of a smooth probability density.  A  possible route to  progress on  the  nonparametric problem was  opened by  noticing  [5]  that  a Bayesian prior for density estimation is  equivalent to  a quantum field  theory  (QFT).  In  particular, there are field  theoretic methods for computing the infinite dimensional analog  of the Occam factor,  at least asymptotically for large numbers of examples.  These obser(cid:173) vations have led to  a number of papers  [6,  7,  8,  9]  exploring alternative formulations and  their implications  for  the  speed  of learning.  Here  we  return  to  the  original  formulation  of Ref.  [5]  and  use  numerical methods to  address  some of the questions left open by  the  analytic work [10]:  What is  the result of balancing the infinite dimensional Occam factor  against the  goodness  of fit?  Is  the  QFT inference optimal  in using alJ  of the  information  relevant for learning [II]? What happens if our learning problem is strongly atypical of the  prior distribution?  Following Ref. [5], if N  i. i. d. samples {Xi}, i  = 1 ... N, are observed, then the probability 
that a particular density Q(x) gave rise to these data is given by  P[Q(x)] rr~1 Q(Xi)