bibtype L - Prototype, methodology, f. module, software
ARLID 0411109
utime 20240103182302.4
mtime 20060210235959.9
ISBN 0-19-852615-6
title (primary) (eng) Lymphoscintigraphy of Upper Limbs: A Bayesian Framework
publisher
place Oxford
name University Press
pub_time 2003
specification
page_count 10 s.
serial
title Bayesian Statistics 7
page_num 543-552
editor
name1 Bernardo
name2 J. M.
editor
name1 Bayarri
name2 M. J.
editor
name1 Berger
name2 J. O.
title (cze) Lymphositygraf horních končetin: Bayesovský přístup
keyword Bayesian estimation
keyword dynamic model identification
keyword quantitative lymphoscintigraphy
author (primary)
ARLID cav_un_auth*0101090
name1 Gebouský
name2 Petr
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101124
name1 Kárný
name2 Miroslav
institution UTIA-B
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0213041
name1 Quinn
name2 A.
country IR
COSATI 06Y
COSATI 12B
cas_special
project
project_id GA102/99/1564
agency GA ČR
ARLID cav_un_auth*0004444
project
project_id IBS1075102
agency GA AV ČR
ARLID cav_un_auth*0014060
project
project_id NC7601
agency GA MZd
ARLID cav_un_auth*0029565
research CEZ:AV0Z1075907
abstract (eng) Lymphoscintigraphy is a diagnostic technique in nuclear medicine used for the investigation of upper limb lymphedema. Typically, only 2 or 3 snapshots of the distribution of radio-tracer in the limb can be obtained. Hence, traditional inferences of important physiological indicators are completely unreliable. The Bayesian paradigm, exploiting available prior information in conjunction with a simplified model of the diffusion dynamics, is used to obtain reliable quantitative evaluations here.
abstract (cze) Aplikace pravděpodobnostních směsí, systém Mixtools
RIV BO
reportyear 2006
department AS
permalink http://hdl.handle.net/11104/0131196
ID_orig UTIA-B 20030096
arlyear 2003
mrcbU10 2003
mrcbU10 Oxford University Press
mrcbU12 0-19-852615-6
mrcbU63 Bayesian Statistics 7 543 552
mrcbU67 Bernardo J. M. 340
mrcbU67 Bayarri M. J. 340
mrcbU67 Berger J. O. 340