Uncertainty Quantification Through Bayesian Nonparametric Modelling

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F20%3A00345811" target="_blank" >RIV/68407700:21110/20:00345811 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Uncertainty Quantification Through Bayesian Nonparametric Modelling
Original language description
Recently there is an increasing endeavour to take into account the underlying uncertainties by stochastic modelling in order to make the numerical predictions as realistic as possible. Uncertainty quantification deals with distinct sources of nondeterminism. A lack of knowledge is expressed by epistemic uncertainties while aleatory uncertainties formulate an inherent randomness. In the case of estimating aleatory uncertainty, the task is to infer unknown but fixed probability density function and the corresponding epistemic uncertainty about this estimation. In order to avoid too strict assumptions about the unknown density function (e.g. prescription of a specific parameterised family of probability density functions), it can be modelled hierarchically by a stochastic process via the Bayesian nonparametric approach. The contribution presents application of a Dirichlet process mixture in modelling the aleatory uncertainty.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA18-04262S" target="_blank" >GA18-04262S: Probabilistic identification of material transport parameters based on non-invasive experimental measurements</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)