Modeling and Estimation of Discrete-Time Reciprocal Processes via Probabilistic Graphical Models
Machine Learning
2016-05-16 v3 Optimization and Control
Abstract
Reciprocal processes are acausal generalizations of Markov processes introduced by Bernstein in 1932. In the literature, a significant amount of attention has been focused on developing dynamical models for reciprocal processes. In this paper, we provide a probabilistic graphical model for reciprocal processes. This leads to a principled solution of the smoothing problem via message passing algorithms. For the finite state space case, convergence analysis is revisited via the Hilbert metric.
Cite
@article{arxiv.1603.04419,
title = {Modeling and Estimation of Discrete-Time Reciprocal Processes via Probabilistic Graphical Models},
author = {Francesca Paola Carli},
journal= {arXiv preprint arXiv:1603.04419},
year = {2016}
}
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31 pages