0361017 20240103195334.720110719235959.9 7 s. www cav_un_epca*0361016978-0-88986-889-21-7Cambridge, UKIASTED2011 factor analysis blind decomvolution image sequences cav_un_auth*0101207 Šmídl Václav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0267768 Tichý Ondřej Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0211467 Šámal M. CZ http://library.utia.cas.cz/separaty/2011/AS/smidl-factor analysis of scintigraphic image sequences with integrated convolution model of factor curves.pdf 303/07/0950 GA ČR CZ cav_un_auth*0273073 CEZ:AV0Z10750506 Factor analysis and deconvolution are commonly used tools in analysis of time activity analysis of biological organs in scintigraphic data. Typically, these are used independently such that the output of the former is taken as an input to the latter. Each method is thus unaware of the restrictions imposed by the other and fails to respect them. In this paper, we propose a probabilistic model that integrates convolution into the factor analysis model. We develop an approximate Bayesian estimation of the model parameters based on Variational Bayes approximation. The new variant of the factor analysis model is suitable for modeling of a range of biological processes where convolution kernels are known to have restricted shapes. Properties of the new model are illustrated on analysis of data from dynamic renal scintigraphy. The proposed model provides more realistic estimates of the convolution kernels. cav_un_auth*0273072 The Second IASTED International Conference on Computational Bioscience Cambridge 11.07.2011-13.07.2011 GB 2012 BB 3 http://hdl.handle.net/11104/0198434 2011 cav_un_epca*0361016 Proceedings of the second international conference on computational bioscience 978-0-88986-889-2 1 7 Cambridge, UK IASTED 2011