Bayesian predictive modeling and comparison of oil samples (pages 52–59)
Paul Blomstedt, Romain Gauriot, Niina Viitala, Tapani Reinikainen and Jukka Corander
Article first published online: 22 NOV 2013 | DOI: 10.1002/cem.2566
We propose a Bayesian predictive approach for evaluating the similarity between the chemical compositions of two oil samples. We derive the underlying statistical model from basic assumptions on modeling assays in analytical chemistry, and, to further facilitate and improve numerical evaluations, develop analytical expressions for the key elements of Bayesian inference for this model. The approach is illustrated with both simulated and real data and is shown to have appealing properties in comparison with both standard frequentist and Bayesian approaches.