Model-based kernel sum rule: kernel Bayesian inference with probabilistic models

DOI10.1007/s10994-019-05852-9zbMath1496.62101arXiv1409.5178OpenAlexW2998466118WikidataQ126411848 ScholiaQ126411848MaRDI QIDQ782449

Yu Nishiyama, Arthur Gretton, Motonobu Kanagawa, Kenji Fukumizu

Publication date: 27 July 2020

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1409.5178

zbMATH Keywords

reproducing kernel Hilbert spacesfilteringkernel methodsstate space modelsprobabilistic modelskernel Bayesian inferencekernel mean embedding

Mathematics Subject Classification ID

Bayesian inference (62F15) Probabilistic models, generic numerical methods in probability and statistics (65C20) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)

Uses Software

• EnKF
• SIFT
• DR-ABC

Cites Work

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• Spatial variation. 2nd ed
• On the empirical estimation of integral probability metrics
• Convergence analysis of deterministic kernel-based quadrature rules in misspecified settings
• Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective
• A modern retrospective on probabilistic numerics
• Probabilistic integration: a role in statistical computation?
• Optimal rates for the regularized least-squares algorithm
• Sequential Monte Carlo Methods in Practice
• Hilbert space embeddings and metrics on probability measures
• Support Vector Machines
• A Hilbert Space Embedding for Distributions
• Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions
• Kernel Mean Embedding of Distributions: A Review and Beyond
• Minimax Estimation of Kernel Mean Embeddings
• Bayesian Probabilistic Numerical Methods
• Robust Filtering and Smoothing with Gaussian Processes
• Probabilistic numerics and uncertainty in computations
• The ensemble Kalman filter for combined state and parameter estimation
• Filtering with State-Observation Examples via Kernel Monte Carlo Filter
• Algorithmic Learning Theory
• Theory of Reproducing Kernels

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