Non-parametric data representation can be done by means of a potential function. This paper introduces a methodology for finding modes of the potential function. Two different me...
We have studied two efficient sampling methods, Langevin and Hessian adapted Metropolis Hastings (MH), applied to a parameter estimation problem of the mathematical model (Lorent...
In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Diri...
In many pattern recognition tasks, given some input data and a family of models, the “best” model is defined as the one which maximizes the likelihood of the data given the m...
Tara N. Sainath, Dimitri Kanevsky, Bhuvana Ramabha...
We propose a video event analysis framework based on object segmentation and tracking, combined with a Hidden Semi-Markov Model (HSMM) that uses state occupancy duration modeling....