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A Joint introduction to Gaussian Processes and Relevance Vector Machines
  with Connections to Kalman filtering and other Kernel Smoothers
v1v2v3v4 (latest)

A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers

Information Fusion (Inf. Fusion), 2020
19 September 2020
Luca Martino
Jesse Read
    BDLGP
ArXiv (abs)PDFHTML

Papers citing "A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers"

11 / 11 papers shown
Data-driven informative priors for Bayesian inference with quasi-periodic data
Data-driven informative priors for Bayesian inference with quasi-periodic dataAstronomical Journal (AJ), 2025
J. Lopez-Santiago
Luca Martino
Joaquín Míguez
Gonzalo Vazquez-Vilar
114
0
0
27 Nov 2025
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABCInternational Statistical Review (ISR), 2021
F. Llorente
Luca Martino
Jesse Read
D. Delgado
OffRL
646
19
0
03 Jan 2025
Hyperbolic Secant representation of the logistic function: Application
  to probabilistic Multiple Instance Learning for CT intracranial hemorrhage
  detection
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection
Francisco M. Castro-Macías
Pablo Morales-Álvarez
Yunan Wu
Rafael Molina
Aggelos K. Katsaggelos
226
7
0
21 Mar 2024
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field
  and Online Inference
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference
Zhidi Lin
Yiyong Sun
Feng Yin
Alexandre Thiéry
524
7
0
10 Dec 2023
Spectral information criterion for automatic elbow detection
Spectral information criterion for automatic elbow detectionExpert systems with applications (ESWA), 2023
Luca Martino
Roberto San Millán-Castillo
E. Morgado
188
15
0
17 Aug 2023
On the safe use of prior densities for Bayesian model selection
On the safe use of prior densities for Bayesian model selection
F. Llorente
Luca Martino
E. Curbelo
J. Lopez-Santiago
D. Delgado
330
23
0
10 Jun 2022
Edge Tracing using Gaussian Process Regression
Edge Tracing using Gaussian Process Regression
Jamie Burke
Stuart King
128
17
0
05 Nov 2021
MCMC-driven importance samplers
MCMC-driven importance samplersApplied Mathematical Modelling (AMM), 2021
F. Llorente
E. Curbelo
Luca Martino
Victor Elvira
D. Delgado
505
13
0
06 May 2021
Deep Importance Sampling based on Regression for Model Inversion and
  Emulation
Deep Importance Sampling based on Regression for Model Inversion and Emulation
F. Llorente
Luca Martino
D. Delgado
G. Camps-Valls
377
22
0
20 Oct 2020
Fast Approximate Multi-output Gaussian Processes
Fast Approximate Multi-output Gaussian Processes
V. Joukov
Dana Kulic
174
9
0
22 Aug 2020
Marginal likelihood computation for model selection and hypothesis
  testing: an extensive review
Marginal likelihood computation for model selection and hypothesis testing: an extensive review
F. Llorente
Luca Martino
D. Delgado
J. Lopez-Santiago
409
106
0
17 May 2020
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