Cristin-resultat-ID: 1971489
Sist endret: 16. februar 2022, 14:31
NVI-rapporteringsår: 2021
Resultat
Vitenskapelig artikkel
2021

Flexible Bayesian Nonlinear Model Configuration

Bidragsytere:
  • Aliaksandr Hubin
  • Geir Storvik og
  • Florian Frommlet

Tidsskrift

The journal of artificial intelligence research
ISSN 1076-9757
e-ISSN 1943-5037
NVI-nivå 2

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2021
Publisert online: 2021
Trykket: 2021
Volum: 72
Sider: 901 - 942
Open Access

Importkilder

Scopus-ID: 2-s2.0-85117758018

Beskrivelse Beskrivelse

Tittel

Flexible Bayesian Nonlinear Model Configuration

Sammendrag

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional flexibility on the possible types of features to be considered. This flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modified mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.

Bidragsytere

Aliaksandr Hubin

  • Tilknyttet:
    Forfatter
    ved Statistisk analyse, maskinlæring og bildeanalyse SAMBA ved Norsk Regnesentral
  • Tilknyttet:
    Forfatter
    ved Matematisk institutt ved Universitetet i Oslo
Aktiv cristin-person

Geir Olve Storvik

Bidragsyterens navn vises på dette resultatet som Geir Storvik
  • Tilknyttet:
    Forfatter
    ved Matematisk institutt ved Universitetet i Oslo

Florian Frommlet

  • Tilknyttet:
    Forfatter
    ved Medizinische Universität Wien
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