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The problem of optimal data collection to efficiently learn the modelparameters of a graphite nitridation experiment is studied in the context ofBayesian analysis using both synthetic and real experimental data. The paperemphasizes that the optimal design can be obtained as a result of aninformation theoretic sensitivity analysis. Thus, the preferred design is wherethe statistical dependence between the model parameters and observables is thehighest possible. In this paper, the statistical dependence between randomvariables is quantified by mutual information and estimated using a k-nearestneighbor based approximation. It is shown, that by monitoring the inferenceprocess via measures such as entropy or Kullback-Leibler divergence, one candetermine when to stop the data collection process. The methodology is appliedto select the most informative designs on both a simulated data set and on anexperimental data set, previously published in the literature. It is also shownthat the sequential Bayesian analysis used in the experimental design can alsobe useful in detecting conflicting information between measurements and modelpredictions.

Bayesian experimental design for the active nitridation of graphite by atomic nitrogen