This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an altern...
Mark Johnson, Thomas L. Griffiths, Sharon Goldwate...
Based on the probabilistic reformulation of principal component analysis (PCA), we consider the problem of determining the number of principal components as a model selection prob...
Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan...
The key task in probabilistic reasoning is to appropriately update one’s beliefs as one obtains new information in the form of evidence. In many application settings, however, th...
The work here explores new numerical methods for supporting a Bayesian approach to parameter estimation of dynamic systems. This is primarily motivated by the goal of providing ac...