Adaptive Quadrature Schemes for Bayesian Inference via Active Learning

Adaptive Quadrature Schemes for Bayesian Inference via Active Learning

We propose novel adaptive quadrature schemes based on an active learning procedure. We consider an interpolative approach for building a surrogate posterior density, combining it with Monte Carlo sampling methods and other quadrature rules. The nodes of the quadrature are sequentially chosen by maxi...

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Bibliographic Details
Main Authors: Fernando Llorente Fernandez, Luca Martino, Victor Elvira, David Delgado, Javier Lopez-Santiago
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Active learning
Bayesian quadrature
emulation
experimental design
Monte Carlo methods
numerical integration
Online Access:https://ieeexplore.ieee.org/document/9260147/

Internet

https://ieeexplore.ieee.org/document/9260147/

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