Likelihood tempering in dynamic model averaging

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00477043" target="_blank" >RIV/67985556:_____/17:00477043 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-54084-9_7" target="_blank" >http://dx.doi.org/10.1007/978-3-319-54084-9_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-54084-9_7" target="_blank" >10.1007/978-3-319-54084-9_7</a>

Result language
angličtina
Original language name
Likelihood tempering in dynamic model averaging
Original language description
We study the problem of online prediction with a set of candidate models using dynamic model averaging procedures. The standard assumptions of model averaging state that the set of admissible models contains the true one(s), and that these models are continuously updated by valid data. However, both these assumptions are often violated in practice. The models used for online tasks are often more or less misspecified and the data corrupted (which is, mathematically, a demonstration of the same problem). Both these factors negatively influence the Bayesian inference and the resulting predictions. In this paper, we propose to suppress these issues by extending the Bayesian update by a sort of likelihood tempering, moderating the impact of observed data to inference. The method is compared to the generic dynamic model averaging and to an alternative solution via sequential quasi-Bayesian mixture modeling.
Czech name
—
Czech description
—

Project
<a href="/en/project/GP14-06678P" target="_blank" >GP14-06678P: Distributed dynamic estimation in diffusion networks</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)