Probabilistic modelling of text data in the bagof-words representation has been dominated by directed graphical models such as pLSI, LDA, NMF, and discrete PCA. Recently, state of...
Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating an...
— We present a new algorithm for scan registration of colored 3D point data which is an extension to the Normal Distributions Transform (NDT). The probabilistic approach of NDT i...
Benjamin Huhle, Martin Magnusson 0002, Wolfgang St...
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
This paper presents a new approach to inference in Bayesian networks. The principal idea is to encode the network by logical sentences and to compile the resulting encoding into an...