… In this work, we present a review of current proposed Bayesian approaches in prognostic …
the Bayesian approaches based on high-dimensional molecular data for prognostic analysis in …

… In this paper a data driven method for RUL prediction based on a Bayesian approach is
proposed. The method builds on unsupervised selection of interesting variables from the input …

… belief network to perform diagnosis and, ultimately, prognosis. We recognize that there are …
for using a Bayesian approach by introducing the fundamentals of Bayes decision theory and …

… In this work, we propose a Bayesian approach based on PF for WTB damage diagnosis and
prognosis due to its numerous advantages [37]. The implementation of PF-based modules …

… Bayesian approach developed in Stingo et al 14 to The Cancer Genome Atlas (TCGA) lung
adenocarcinoma data to identify prognostic … First, it conducts a Bayesian analysis of the lung …

… prognosis of a system using a dynamic Bayesian network (… Bayesian networks are suitable
for probabilistic prognosis … time of prognosis, tP, the first time point for which a prognosis …

… In this section, we describe the development of the carcinoid model; a dynamic Bayesian
network for the prognosis of patients that present with a carcinoid tumor. The carcinoid model …

… Commonly used techniques are simple decision rules based on the categorization of a
prognostic score, Bayes’ rule, and logistic regression. The parameters of these models are …

… method to a dataset containing gene expression profiles of brain tumors in order to identify
molecular and genetic signatures that could be of prognostic … is in finding prognostic groups of …

… The most significant challenges for making prognostic pre… the battery prognostic problem in
a Bayesian learning (RVM–… a refinement of the PF prognostic framework to further reduce the …