On Bayesian Decision-Making and Approximation of Probability Densities

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F15%3APU113923" target="_blank" >RIV/00216305:26620/15:PU113923 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7296313" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7296313</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2015.7296313" target="_blank" >10.1109/TSP.2015.7296313</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Bayesian Decision-Making and Approximation of Probability Densities

Popis výsledku v původním jazyce

An approximation of a true, unknown, posterior probability density (pd) representing some real state-space systém is presented as Bayesian decision-making among a set of possible descriptions (models). The decision problem is carefully defined on its basic elements and it is shown how it leads to the use of the Kullback-Leibler (KL) divergence for evaluating a loss of information between the unknown posterior pd and its approximation. The resulting algorithm is derived on a general level, allowing specific algorithms to be designed according to a selected class of the probability distributions. A concrete example of the algorithm is proposed for the Gaussian case. An experiment is performed assuming that none of the possible descriptions is precisely identical with the unknown system.

Název v anglickém jazyce

On Bayesian Decision-Making and Approximation of Probability Densities

Popis výsledku anglicky

An approximation of a true, unknown, posterior probability density (pd) representing some real state-space systém is presented as Bayesian decision-making among a set of possible descriptions (models). The decision problem is carefully defined on its basic elements and it is shown how it leads to the use of the Kullback-Leibler (KL) divergence for evaluating a loss of information between the unknown posterior pd and its approximation. The resulting algorithm is derived on a general level, allowing specific algorithms to be designed according to a selected class of the probability distributions. A concrete example of the algorithm is proposed for the Gaussian case. An experiment is performed assuming that none of the possible descriptions is precisely identical with the unknown system.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/ED1.1.00%2F02.0068" target="_blank" >ED1.1.00/02.0068: CEITEC - central european institute of technology</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

38th International Conference on Telecommunications and Signal Processing (TSP)

ISBN

978-1-4799-8498-5

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

499-503

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Prague

Místo konání akce

Prague, Czech Republic

Datum konání akce

9. 7. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000375231000204