Volume 7, Issue Supplement 1, January 2014, Pages 6 - 17
Implicit parameter estimation for conditional Gaussian Bayesian networks
Aida Jarraya, Philippe Leray, Afif Masmoudi
Received 12 June 2012, Accepted 7 August 2013, Available Online 1 January 2014.
- https://doi.org/10.1080/18756891.2014.853926How to use a DOI?
- Conditional Gaussian Bayesian networks, Bayesian estimation, Implicit estimation, Parameter learning
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Aida Jarraya AU - Philippe Leray AU - Afif Masmoudi PY - 2014 DA - 2014/01 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 - https://doi.org/10.1080/18756891.2014.853926 ID - Jarraya2014 ER -