16 search results - page 2 / 4 » Bayesian Inference: An Introduction to Principles and Practi... |
AI
2010
Springer
13 years 5 months ago
2010
Springer
Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
ECML
2005
Springer
13 years 10 months ago
2005
Springer
Abstract. In this paper we consider latent variable models and introduce a new U-likelihood concept for estimating the distribution over hidden variables. One can derive an estimat...
ICML
2008
IEEE
14 years 6 months ago
2008
IEEE
We present a principled Bayesian framework for modeling partial memberships of data points to clusters. Unlike a standard mixture model which assumes that each data point belongs ...
PAMI
2006
13 years 5 months ago
2006
Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
Book
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...