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Variational Fourier features for Gaussian processes
This work brings together two powerful concepts in Gaussian processes: the variational
approach to sparse approximation and the spectral representation of Gaussian processes …
approach to sparse approximation and the spectral representation of Gaussian processes …
A Gaussian process latent force model for joint input-state estimation in linear structural systems
The problem of combined state and input estimation of linear structural systems based on
measured responses and a priori knowledge of structural model is considered. A novel …
measured responses and a priori knowledge of structural model is considered. A novel …
Random feature expansions for deep Gaussian processes
The composition of multiple Gaussian Processes as a Deep Gaussian Process DGP
enables a deep probabilistic nonparametric approach to flexibly tackle complex machine …
enables a deep probabilistic nonparametric approach to flexibly tackle complex machine …
Bayesian kernelized matrix factorization for spatiotemporal traffic data imputation and kriging
Missingness and corruption are common problems for real-world traffic data. How to
accurately perform imputation and prediction based on incomplete or even sparse traffic …
accurately perform imputation and prediction based on incomplete or even sparse traffic …
MCMC for variationally sparse Gaussian processes
Gaussian process (GP) models form a core part of probabilistic machine learning.
Considerable research effort has been made into attacking three issues with GP models …
Considerable research effort has been made into attacking three issues with GP models …
Preconditioning kernel matrices
The computational and storage complexity of kernel machines presents the primary barrier
to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to …
to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to …
Kernelized Bayesian matrix factorization
We extend kernelized matrix factorization with a fully Bayesian treatment and with an ability
to work with multiple side information sources expressed as different kernels. Kernel …
to work with multiple side information sources expressed as different kernels. Kernel …
Extraction of contact-point response in indirect bridge health monitoring using an input estimation approach
Identification of bridge dynamic properties from moving vehicle responses presents several
practical benefits. However, a problem that arises when working with vehicle responses for …
practical benefits. However, a problem that arises when working with vehicle responses for …
A statistical review of Template Model Builder: a flexible tool for spatial modelling
The integrated nested Laplace approximation (INLA) is a well‐known and popular technique
for spatial modelling with a user‐friendly interface in the R‐INLA package. Unfortunately …
for spatial modelling with a user‐friendly interface in the R‐INLA package. Unfortunately …
Gaussianprocesses. jl: A nonparametric bayes package for the julia language
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely
used across the sciences, and in industry, to model complex data sources. Key to applying …
used across the sciences, and in industry, to model complex data sources. Key to applying …