Now showing items 1-5 of 5
Characterizing response behavior in multisensory perception with conflicting cues
We explore a recently proposed mixture model approach to understanding interactions between conflicting sensory cues. Alternative model formulations, differing in their sensory noise models and inference methods, are ...
Evaluation methods for topic models
A natural evaluation metric for statistical topic models is the probability of held-out documents given a trained model. While exact computation of this probability is intractable, several estimators for this probability ...
Evaluating probabilities under high-dimensional latent variable models
We present a simple new Monte Carlo algorithm for evaluating probabilities of observations in complex latent variable models, such as Deep Belief Networks. While the method is based on Markov chains, estimates based on ...
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities
The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution ...
Gaussian processes and fast matrix-vector multiplies
(Numerical Mathematics in Machine Learning Workshop - International Conference on Machine Learning ICML 2009, 2009)
Gaussian processes (GPs) provide a flexible framework for probabilistic regression. The necessary computations involve standard matrix operations. There have been several attempts to accelerate these operations based on ...