Optimised conjugate prior for model structure estimation in the exponential family

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00619365" target="_blank" >RIV/67985556:_____/25:00619365 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0957417425013387?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417425013387?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2025.127716" target="_blank" >10.1016/j.eswa.2025.127716</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimised conjugate prior for model structure estimation in the exponential family

Popis výsledku v původním jazyce

Model structure estimation has gained attention owing to the challenge of analysing large, scarce, and poorly informative data. Bayesian hypothesis testing formally addresses this issue. For nested model structures, an efficient search method provides the maximum a posteriori (MAP) estimate, even in extensive hypothesis spaces. However, estimation quality highly depends on prior probability densities of the unknown, hypothesis-specific parameters. Existing solutions mitigate this issue by estimating multivariate hyperparameters of thesenpriors, however, these solutions restrict the hyperparameter space, limiting estimation quality. This study enhances model structure estimation for exponential family models by imposing minimal constraints on the selected hyperparameter. For Gaussian models with linearly weighted auto-regression and regression variables, the MAP hyperparameter estimate is analytic and requires solving only one equation for a scalar variable. Experiments, including a complex simulation and multi-step forecasting of futures prices, confirm the solutionnquality gains.

Název v anglickém jazyce

Optimised conjugate prior for model structure estimation in the exponential family

Popis výsledku anglicky

Model structure estimation has gained attention owing to the challenge of analysing large, scarce, and poorly informative data. Bayesian hypothesis testing formally addresses this issue. For nested model structures, an efficient search method provides the maximum a posteriori (MAP) estimate, even in extensive hypothesis spaces. However, estimation quality highly depends on prior probability densities of the unknown, hypothesis-specific parameters. Existing solutions mitigate this issue by estimating multivariate hyperparameters of thesenpriors, however, these solutions restrict the hyperparameter space, limiting estimation quality. This study enhances model structure estimation for exponential family models by imposing minimal constraints on the selected hyperparameter. For Gaussian models with linearly weighted auto-regression and regression variables, the MAP hyperparameter estimate is analytic and requires solving only one equation for a scalar variable. Experiments, including a complex simulation and multi-step forecasting of futures prices, confirm the solutionnquality gains.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Expert Systems With Applications

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

283

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

127716

Kód UT WoS článku

001487744100001

EID výsledku v databázi Scopus

2-s2.0-105004009861