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...
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: | |
Online Access: | https://ieeexplore.ieee.org/document/9260147/ |
Similar Items
-
Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design
by: Zhihang Xu, et al.
Published: (2020-02-01) -
Quadrature by differentiation
by: Macnaughton, Robert Frank
Published: (2019) -
Monte Carlo integration.
Published: (1993) -
Bayesian Inference for Stochastic Volatility Models
by: Men, Zhongxian
Published: (2012) -
Bayesian Inference for Stochastic Volatility Models
by: Men, Zhongxian
Published: (2012)