Results 41 to 50 of about 14,251,254 (300)
Forecasting in dynamic factor models using Bayesian model averaging [PDF]
, 2004 This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given.
Koop, G.M., Potter, S.
core +4 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Loneliness is associated with adverse physical and mental health outcomes and remains understudied in children and adolescents undergoing cancer therapy. Pediatric oncology patients may be at increased risk due to medical isolation and disruption of social networks.
Charlotte N. Stahlfeld +5 more
wiley +1 more source
Two-Population Mortality Forecasting: An Approach Based on Model Averaging
RisksThe analysis of residual life expectancy evolution at retirement age holds great importance for life insurers and pension schemes. Over the last 30 years, numerous models for forecasting mortality have been introduced, and those that allow us to predict ...
Luca De Mori +3 more
doaj +1 more source
Averaged Extended Tree Augmented Naive Classifier
Entropy, 2015 This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence ...
Aaron Meehan, Cassio P. de Campos
doaj +1 more source
Central Nervous System Tumors Among Infants in Canada: A Report From CYP‐C
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Central nervous system (CNS) tumors in infants are rare, pose unique clinical challenges, and lack large‐scale evidence‐based data to guide management. This study seeks to describe CNS tumors in Canadian infants and to compare their outcomes with those of older children.
Samuel Sassine +17 more
wiley +1 more source
Mixtures of g-priors for Bayesian model averaging with economic applications [PDF]
, 2010 We examine the issue of variable selection in linear regression modeling, where we have a potentially large amount of possible covariates and economic theory offers insufficient guidance on how to select the ap- propriate subset.
Ley, Eduardo +2 more
core +1 more source
Bayesian curve estimation by model averaging [PDF]
, 2003 A bayesian approach is used to estimate a nonparametric regression model. The main features of the procedure are, first, the functional form of the curve is approximated by a mixture of local polynomials by Bayesian Model Averaging (BMA); second, the ...
Peña, Daniel, Redondas, María Dolores
core +1 more source
Comparative Drug Response Profiling in Neuroblastoma Cell Lines and Patient‐Derived Tumor Organoids
Pediatric Blood &Cancer, EarlyView.ABSTRACT High‐risk neuroblastoma remains a leading cause of pediatric cancer mortality, and improved preclinical models are needed to guide therapeutic developments. We screened seven high‐risk neuroblastoma cell lines and three patient‐derived tumor organoids with 528 compounds alongside bone marrow controls, and compared them with external datasets ...
Krzysztof Wierbiłowicz +12 more
wiley +1 more source
Bagged Averaging of Regression Models [PDF]
, 2006 Linear regression and regression tree models are among the most known regression models used in the machine learning community and recently many researchers have examined their sufficiency in ensembles. Although many methods of ensemble design have been proposed, there is as yet no obvious picture of which method is best.
Sotiris B. Kotsiantis +2 more
openaire +2 more sources
MathematicsBayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques.
Aliaksandr Hubin, Geir Storvik
doaj +1 more source