Title :
Connections Between Score Matching, Contrastive Divergence, and Pseudolikelihood for Continuous-Valued Variables
Author_Institution :
Helsinki Univ., Helsinki
Abstract :
Score matching (SM) and contrastive divergence (CD) are two recently proposed methods for estimation of nonnormalized statistical methods without computation of the normalization constant (partition function). Although they are based on very different approaches, we show in this letter that they are equivalent in a special case: in the limit of infinitesimal noise in a specific Monte Carlo method. Further, we show how these methods can be interpreted as approximations of pseudolikelihood.
Keywords :
Monte Carlo methods; maximum likelihood estimation; Monte Carlo method; continuous-valued variables; contrastive divergence; nonnormalized statistical methods estimation; normalization constant; pseudolikelihood approximations; score matching; Computer science; Information technology; Monte Carlo methods; Parameter estimation; Probability density function; Samarium; Statistical analysis; Normalization constant; partition function; statistical estimation; Algorithms; Artificial Intelligence; Computer Simulation; Likelihood Functions; Models, Statistical; Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.895819