Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

Bayesian regression with multivariate linear splines

Bayesian Robust Multivariate Linear Regression With Incomplete Data

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Bayesian multivariate logistic regression.

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...

Estimating Uncertainty of Streamflow Simulation using Bayesian Neural Networks

Recent studies have shown that Bayesian Neural Networks (BNNs) are powerful tools for providing reliable hydrologic prediction and quantifying the prediction uncertainty. The reasonable estimation of the prediction uncertainty, a valuable for decision making to address water resources management and design problems, is influenced by the techniques used to deal with different uncertainty sources...

Bayesian quantile regression using random B-spline series prior

A Bayesian method for simultaneous quantile regression on a real variable is considered. By monotone transformation, the response variable and the predictor variable are transformed into the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions ξ1 and ξ2 not depending on the prediction variables. In a Bayesian approach, a prio...