Statistics in ROOT
... TH1::Fit (binned data) – Chisquare and Loglikelihood methods TGraph::Fit (unbinned data) TGraphErrors::Fit (data with errors) TGraphAsymmErrors::Fit (taking into account asymmetry of errors) TTree::Fit and TTree::UnbinnedFit ...
... TH1::Fit (binned data) – Chisquare and Loglikelihood methods TGraph::Fit (unbinned data) TGraphErrors::Fit (data with errors) TGraphAsymmErrors::Fit (taking into account asymmetry of errors) TTree::Fit and TTree::UnbinnedFit ...
Latent Gaussian Processes for Distribution Estimation of Multivariate
... Our proposed model can also be seen as a discrete extension of the Gaussian process latent variable model (GPLVM, Lawrence, 2005). This model has been proposed recently as means of performing non-linear dimensionality reduction (counterpart to the linear principal component analysis (Tipping & Bisho ...
... Our proposed model can also be seen as a discrete extension of the Gaussian process latent variable model (GPLVM, Lawrence, 2005). This model has been proposed recently as means of performing non-linear dimensionality reduction (counterpart to the linear principal component analysis (Tipping & Bisho ...
Lecture 5 - Categorical and Survival Analyses
... What is Survival Analysis? • Survival analysis is a collection of statistical analysis techniques where the outcome is time to an event. • Survival or time-to-event outcomes are defined by the pair of random variables (ti, δi) that give the observation time and an indicator of whether or not the ev ...
... What is Survival Analysis? • Survival analysis is a collection of statistical analysis techniques where the outcome is time to an event. • Survival or time-to-event outcomes are defined by the pair of random variables (ti, δi) that give the observation time and an indicator of whether or not the ev ...
An Inference-based Prognostic Framework for Health
... parameters are directly measured from the system or via a fusion of multiple parameters (Coble, 2010). A survey of data-driven prognostics is provided by Schwabacher (2005). Si, Wang, Hu, and Zhou (2011) presented a detailed review of the statistical data-driven approaches. Gorjian, Ma, Mittinty, Ya ...
... parameters are directly measured from the system or via a fusion of multiple parameters (Coble, 2010). A survey of data-driven prognostics is provided by Schwabacher (2005). Si, Wang, Hu, and Zhou (2011) presented a detailed review of the statistical data-driven approaches. Gorjian, Ma, Mittinty, Ya ...
Proceedings of the 2013 Winter Simulation Conference
... In the presence of non-Gaussian noise the least squares estimator for the parameters of a regression model can be suboptimal. Therefore, it is reasonable to consider other norms. L p -norm estimators are a useful alternative, particularly when the residuals are heavy-tailed. We analyze the convergen ...
... In the presence of non-Gaussian noise the least squares estimator for the parameters of a regression model can be suboptimal. Therefore, it is reasonable to consider other norms. L p -norm estimators are a useful alternative, particularly when the residuals are heavy-tailed. We analyze the convergen ...
