Browsing by Author "Grientschnig, D."
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- ItemA Formalism for Expressing the Probability Density Functions of Interrelated Quantities(2013) Lira Canguilhem, Ignacio; Grientschnig, D.
- ItemAssignment of a non-informative prior when using a calibration function(2012) Lira, I.; Grientschnig, D.The evaluation of measurement uncertainty associated with the use of calibration functions was addressed in a talk at the 19th IMEKO World Congress 2009 in Lisbon (Proceedings, pp 2346-51). Therein, an example involving a cubic function was analysed by a Bayesian approach and by the Monte Carlo method described in Supplement 1 to the 'Guide to the Expression of Uncertainty in Measurement'. Results were found to be discrepant. In this paper we examine a simplified version of the example and show that the reported discrepancy is caused by the choice of the prior in the Bayesian analysis, which does not conform to formal rules for encoding the absence of prior knowledge. Two options for assigning a non-informative prior free from this shortcoming are considered; they are shown to be equivalent.
- ItemBayesian Analysis of a Simple Measurement Model Distinguishing between Types of Information(2015) Lira Canguilhem, Ignacio; Grientschnig, D.
- ItemCombining Probability Distributions by Multiplication in Metrology : A Viable Method?(2014) Grientschnig, D.; Lira Canguilhem, Ignacio
- ItemDeriving PDFs for Interrelated Quantities : What to Do If There Is "More Than Enough" Information?(2014) Lira Canguilhem, Ignacio; Grientschnig, D.
- ItemEquivalence of alternative Bayesian procedures for evaluating measurement uncertainty(IOP PUBLISHING LTD, 2010) Lira, I.; Grientschnig, D.Current recommendations for evaluating uncertainty of measurement are based on the Bayesian interpretation of probability distributions as encoding the state of knowledge about the quantities to which those distributions refer. Given a measurement model that relates an output quantity to one or more input quantities, the distribution of the former is obtained by propagating those of the latter according to the axioms of probability calculus and also, if measurement data are available, by applying Bayes' theorem.
- ItemError-in-variables models in calibration(2017) Lira Canguilhem, Ignacio; Grientschnig, D.