The Cluster-Weighted Modeling (CWM) is emerging as a versatile tool for modeling dynamical systems. It is a mixture density estimator around local models. To be specific, the inpu...
In this paper we propose a Gaussian-kernel-based online kernel density estimation which can be used for applications of online probability density estimation and online learning. ...
In this paper, we develop a novel online algorithm based on the Sequential Monte Carlo (SMC) samplers framework for posterior inference in Dirichlet Process Mixtures (DPM) (DelMor...
We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by i...
We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a repr...