Implicit parameter estimation for conditional Gaussian Bayesian networks

DOI

10.1080/18756891.2014.853926How to use a DOI?

Keywords

Conditional Gaussian Bayesian networks, Bayesian estimation, Implicit estimation, Parameter learning

Abstract

The Bayesian estimation of the conditional Gaussian parameter needs to define several a priori parameters. The proposed approach is free from this definition of priors. We use the Implicit estimation method for learning from observations without a prior knowledge. We illustrate the interest of such an estimation method by giving first the Bayesian Expectation A Posteriori estimator for conditional Gaussian parameters. Then, we describe the Implicit estimators for the same parameters. Moreover, an experimental study is proposed in order to compare both approaches.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Aida Jarraya
AU  - Philippe Leray
AU  - Afif Masmoudi
PY  - 2014
DA  - 2014/01/01
TI  - Implicit parameter estimation for conditional Gaussian Bayesian networks
JO  - International Journal of Computational Intelligence Systems
SP  - 6
EP  - 17
VL  - 7
IS  - Supplement 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.853926
DO  - 10.1080/18756891.2014.853926
ID  - Jarraya2014
ER  -