Filtering with State-Observation Examples via Kernel Monte Carlo Filter

DOI10.1162/NECO_a_00806zbMath1418.62361arXiv1312.4664WikidataQ40225681 ScholiaQ40225681MaRDI QIDQ5380397

Yu Nishiyama, Motonobu Kanagawa, Kenji Fukumizu, Arthur Gretton

Publication date: 4 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1312.4664

zbMATH Keywords

filteringstate-space model

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Inference from stochastic processes and prediction (62M20) Applications of statistics to physics (62P35)

Related Items (3)

Convergence analysis of deterministic kernel-based quadrature rules in misspecified settings ⋮ Model-based kernel sum rule: kernel Bayesian inference with probabilistic models ⋮ Unnamed Item

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Filtering via approximate Bayesian computation
• Kernel methods in machine learning
• Consistent nonparametric regression. Discussion
• Optimal regression rates for SVMs using Gaussian kernels
• Asymptotic behavior of the eigenvalues of certain integral equations. II
• Sequential Monte Carlo Methods in Practice
• Hilbert space embeddings and metrics on probability measures
• 10.1162/153244303768966085
• 10.1162/15324430260185619
• Support Vector Machines
• A Hilbert Space Embedding for Distributions
• Asymptotic Behavior of the Eigenvalues of Certain Integral Equations
• Theory of Reproducing Kernels
• Monte Carlo strategies in scientific computing
• New analysis and results for the Frank-Wolfe method

This page was built for publication: Filtering with State-Observation Examples via Kernel Monte Carlo Filter