Likelihood tempering in dynamic model averaging

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Likelihood tempering in dynamic model averaging

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Likelihood tempering in dynamic model averaging

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GP14-06678P" target="_blank" >GP14-06678P: Distribuované dynamické odhadování v difuzních sítích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Bayesian Statistics in Action

ISBN

978-3-319-54083-2

ISSN

2194-1009

e-ISSN

—

Počet stran výsledku

11

Strana od-do

67-77

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Florence

Datum konání akce

19. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000418403500007