A Bayesian Approach to Predict Solubility Parameters

A BAYESIAN APPROACH TO COMPUTING MISSING REGRESSOR VALUES

In this article, Lindley's measure of average information is used to measure the information contained in incomplete observations on the vector of unknown regression coefficients [9]. This measure of information may be used to compute the missing regressor values.

A Bayesian Approach to Estimation of Speaker Normalization Parameters

In this work, a Bayesian approach to speaker normalization is proposed to compensate for the degradation in performance of a speaker independent speech recognition system. The speaker normalization method proposed herein uses the technique of vocal tract length normalization (VTLN). The VTLN parameters are estimated using a novel Bayesian approach which utilizes the Gibbs sampler, a special typ...

Random Forest Models To Predict Aqueous Solubility

Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offer...

Bayesian approach to inference of population structure

Methods of inferring the population structure‎, ‎its applications in identifying disease models as well as foresighting the physical and mental situation of human beings have been finding ever-increasing importance‎. ‎In this article‎, ‎first‎, ‎motivation and significance of studying the problem of population structure is explained‎. ‎In the next section‎, ‎the applications of inference of p...

Solubility and Dissolution of Amorphous Pharmaceuticals—A Novel Thermodynamic Approach to Predict the Performance In Vitro