%0 Generic %1 citeulike:1528136 %A van der Merwe, R. %D 2003 %K kalman-filter time-series %T Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models %U http://citeseer.ist.psu.edu/vandermerwe03sigmapoint.html %X Probabilistic inference is the problem of estimating the hidden states of a system in an optimal and consistent fashion given a set of noisy or incomplete observations. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the posterior density of the system state as new observations arrive online.

@misc{citeulike:1528136, abstract = {{Probabilistic inference is the problem of estimating the hidden states of a system in an optimal and consistent fashion given a set of noisy or incomplete observations. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the posterior density of the system state as new observations arrive online.}}, added-at = {2019-06-18T20:47:03.000+0200}, author = {van der Merwe, R.}, biburl = {https://www.bibsonomy.org/bibtex/2c7b1e88d721ea4660c570b3e1eec6259/alexv}, citeulike-article-id = {1528136}, citeulike-linkout-0 = {http://citeseer.ist.psu.edu/vandermerwe03sigmapoint.html}, citeulike-linkout-1 = {http://citeseer.lcs.mit.edu/vandermerwe03sigmapoint.html}, citeulike-linkout-2 = {http://citeseer.ifi.unizh.ch/vandermerwe03sigmapoint.html}, citeulike-linkout-3 = {http://citeseer.comp.nus.edu.sg/vandermerwe03sigmapoint.html}, interhash = {2d8784aee32872a40344ba2c9069377a}, intrahash = {c7b1e88d721ea4660c570b3e1eec6259}, keywords = {kalman-filter time-series}, posted-at = {2007-08-01 17:32:31}, priority = {0}, timestamp = {2019-08-24T00:23:17.000+0200}, title = {{Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models}}, url = {http://citeseer.ist.psu.edu/vandermerwe03sigmapoint.html}, year = 2003 }