Copolymerization Reactivity Ratio Inference: Determining Confidence Contours in Parameter Space via a Bayesian Hierarchical Approach

Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate estimations of nonlinear nature or complex error structures, the procedure determining confidence is statistically task. polymer chemists, such particular cases encountered determination reactivity ratios copolymerization. Hereby, copolymerization requires estimation. Additionally, data may possess (possibly correlated) errors both dependent independent variables. A common approach for error-in-variables model yielding unbiased Regarding ratios, date published procedures neglect non-Gaussian structure estimates that consequence nonlinearity model. In this publication, issue addressed by employing Bayesian hierarchical model, which correctly propagates all The statistical discussed chemist friendly language encourage confident usage tool. based on Python program requiring minimal installation effort. detailed manual code included appendix work, an effort make available interested chemists.