Thu, 24 Jul 2008 15:18:09 BST CiteULike: dcastros bayesian http://www.citeulike.org/user/dcastro/tag/bayesian CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/dcastro/article/142938 <i>(# 1995)</i><br /><br />A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to... A tutorial on learning with bayesian networks D Heckerman (# 1995) 2005-03-30T13:29:42-00:00 bayesian learning network tutorial http://www.citeulike.org/user/dcastro/article/431128 <i>Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], Vol. 50, No. 2. (2002), pp. 174-188.</i><br /><br />Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking MS Arulampalam S Maskell N Gordon T Clapp doi:10.1109/78.978374 Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], Vol. 50, No. 2. (2002), pp. 174-188. 2005-12-09T09:18:13-00:00 2002 Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on] 50 2 174 188 bayesian filter particle tracking