Model-based kernel sum rule: kernel Bayesian inference with probabilistic models
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models
Abstract Kernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and …