Mixtures of Self-Modeling Regressions

Convergence of Posterior Distribution in the Mixture of Regressions

Mixture models provide a method of modeling a complex probability distribution in terms of simpler structures. In particular, the method of mixture of regressions has received considerable attention due to its modeling flexibility and availability of convenient computational algorithms. While the theoretical justification has been successfully worked out from the frequentist point of view, its ...

Determination and Modeling of Activity Coefficients of Sodium Chloride in (Glycerol + Water) Mixtures Based on Potentiometric Measurements

In this work, the results concerning to the mean activity coefficient measurements for NaCl in the (glycerol + water) system using the potentiometric method are reported. The potentiometric measurements were performed on the galvanic cells without liquid junction of the type: Ag|AgCl|NaCl (m), glycerol (wt%), H2O (1 - wt)%|Na-ISE, in various mixed solvent systems containing 0, 5, 10,...

Erratum to: Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions

Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both theoretical and numerical points of view; in particular, we show that Gaussian CWM includes mixtures of distributions and mixtures of regressions as special cases. Further, we in...

Study of Surface Tension of Binary Mixtures of Poly (Ethylene Glycol) in Water and Poly (Propylene Glycol) in Ethanol and its Modeling Using Neural Network

MODELING OF FLOW NUMBER OF ASPHALT MIXTURES USING A MULTI–KERNEL BASED SUPPORT VECTOR MACHINE APPROACH

Flow number of asphalt–aggregate mixtures as an explanatory factor has been proposed in order to assess the rutting potential of asphalt mixtures. This study proposes a multiple–kernel based support vector machine (MK–SVM) approach for modeling of flow number of asphalt mixtures. The MK–SVM approach consists of weighted least squares–support vector machine (WLS–SVM) integrating two kernel funct...