109 search results - page 4 / 22 » Bayesian Model Selection when the Number of Components is Un... |
IBPRIA
2007
Springer
14 years 1 months ago
2007
Springer
Abstract. This paper presents an algorithm for the estimation of multiple regions with unknown shapes and positions using multiple active contour models (ACM’s). The algorithm or...
AAAI
2004
13 years 9 months ago
2004
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...
ICASSP
2011
IEEE
12 years 11 months ago
2011
IEEE
In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Diri...
CISS
2008
IEEE
14 years 2 months ago
2008
IEEE
—In this paper we present an Information Theoretic Estimator for the number of sources mutually disjoint in a linear mixing model. The approach follows the Minimum Description Le...
JMLR
2010
13 years 2 months ago
2010
Bayesian principal component analysis (BPCA), a probabilistic reformulation of PCA with Bayesian model selection, is a systematic approach to determining the number of essential p...