In this paper we argue that the problem is that the BDe metric is based on assumptions about the BN model parameters distribution assumed to generate the data ...
The marginal likelihood of the data computed using Bayesian score metrics is at the core of score+search methods when learning Bayesian networks from data.
Abstract. The marginal likelihood of the data computed using Bayesian score metrics is at the core of score+search methods when learning.
Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks · 14 Citations · 22 References.
Oct 16, 2012 · This paper is devoted to analyzing the performance of the BDe metric and justifying its improvement when the ESS parameter is locally ...
Nov 24, 2015 · The marginal likelihood of the data computed using Bayesian score metrics is at the core of score+search methods when learning Bayesian ...
Nov 10, 2023 · We propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models.
Nov 2, 2023 · We propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models.
Missing: Metrics. | Show results with:Metrics.
Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks · Computer Science, Mathematics. Int. J. Approx.
Locally Averaged Bayesian Dirichlet Metrics for Learning the Structure and the Parameters of Bayesian Networks. International Journal of Approximate ...