Linear Inverse Problem with Range Prior on Correlations and Its Variational Bayes Inference
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00474383" target="_blank" >RIV/67985556:_____/17:00474383 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-54084-9_9" target="_blank" >http://dx.doi.org/10.1007/978-3-319-54084-9_9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-54084-9_9" target="_blank" >10.1007/978-3-319-54084-9_9</a>
Alternative languages
Result language
angličtina
Original language name
Linear Inverse Problem with Range Prior on Correlations and Its Variational Bayes Inference
Original language description
The choice of regularization for an ill-conditioned linear inverse problem has significant impact on the resulting estimates. We consider a linear inverse model with on the solution in the form of zero mean Gaussian prior and with covariance matrix represented in modified Cholesky form. Elements of the covariance are considered as hyper-parameters with truncated Gaussian prior. The truncation points are obtained from expert judgment as range on correlations of selected elements of the solution. This model is motivated by estimation of mixture of radionuclides from gamma dose rate measurements under the prior knowledge on range of their ratios. Since we aim at high dimensional problems, we use the Variational Bayes inference procedure to derive approximate inference of the model. The method is illustrated and compared on a simple example and on more realistic 6 hours long release of mixture of 3 radionuclides.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/7F14287" target="_blank" >7F14287: Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI)</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Bayesian Statistics in Action: BAYSM 2016
ISBN
978-3-319-54084-9
ISSN
2194-1009
e-ISSN
2194-1017
Number of pages
11
Pages from-to
91-101
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Florence
Event date
Jun 19, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000418403500009