We develop a Bayesian uncertainty quantification framework using a local binary tree surrogate model that is able to make use of arbitrary Bayesian regression methods. The tree is adaptively constructed using information about the sensitivity of the response and is biased by the underlying input probability distribution. The local Bayesian regressions are based on a reformulation of the relevan...

In recent years, kernel methods have demonstrated their performance in hyperspectral imaging. Among the reasons their ability to handle large input spaces is essential. However for this type of applications a critical problem is the choice of the kernel which must combine spectral and spatial information [1] and of course achieve good generalization performance. The kernel design stage is gener...

a central problem ill empirical macroeconomics is to determine when and how much the exchange rate is misaligned. this paper clarifies and calculates the concept of’ the equilibrium real exchange rate, using a structural vector auto regression (var) model. by imposing long—run restrictions on a var model for iran, lour structural shocks are identified: nominal demand, real demand, supply and oi...

Inherent process and measurement uncertainty has posed a challenging issue on soft sensor development of batch bioprocesses. In this paper, a new soft sensor modeling framework is proposed by integrating Bayesian inference strategy with two-stage support vector regression (SVR) method. The Bayesian inference procedure is first designed to identify measurement biases and misalignments via poster...