Gaussian Process Networks

@inproceedings{citeulike:2995232, abstract = {In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures. In the Bayesian framework, this is done by evaluating the marginal likelihood of the data given a candidate structure. This term can be computed in closed-form for standard parametric families (e.g., Gaussians), and can be approximated, at some computational cost, for some semi-parametric ...}, author = {Friedman, Nir and Nachman, Iftach }, citeulike-article-id = {2995232}, keywords = {bayesian, bayesian-networks, networks}, pages = {211--219}, posted-at = {2008-07-12 12:08:26}, priority = {2}, title = {Gaussian Process Networks}, url = {http://citeseer.ist.psu.edu/friedman00gaussian.html} }

TY - CONF ID - citeulike:2995232 L3 - citeulike-article-id:2995232 TI - Gaussian Process Networks SP - 211 EP - 219 N2 - In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures. In the Bayesian framework, this is done by evaluating the marginal likelihood of the data given a candidate structure. This term can be computed in closed-form for standard parametric families (e.g., Gaussians), and can be approximated, at some computational cost, for some semi-parametric ... KW - bayesian KW - bayesian-networks KW - networks AU - Friedman, Nir AU - Nachman, Iftach UR - http://citeseer.ist.psu.edu/friedman00gaussian.html ER -