Bayesian inference for wind field retrieval

2 Bayesian Inference for Wind Field Retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Pre...

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We use a Gaussian process with hyper-parameters estimated from Numerical Weather Predicti...

Bayesian Inference for Agricultural Field

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

The Field-wise Wind Retrieval Objective Function

The Field-wise Objective Function is an important concept in eldwise wind retrieval. A discussion on the purpose of the eld-wise objective function is presented. Several variations of the eld-wise objective function are compared. The gradient vectors and Hessian matrices are derived for each variation. 1 An Overview of Field-wise Objective Functions An objective function is an error metric, pro...