Optimised conjugate prior for model structure estimation in the exponential family

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Optimised conjugate prior for model structure estimation in the exponential family
Original language description
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.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)