We have described the implementation of a graphical programming tool in the object-oriented language, Smalltalk-80, that allows a user to construct a radiographic measurement model. The measurement model can be used to generate the measurements predicted by a given parameterized model of an experimental object. In this paper, we describe extensions to the graphical programming tool that allow it to be used to perform Bayesian inference on very large sets of object parameters, given real experimental data, by optimizing the likelihood or posterior probability of the parameters, given the real data.
Keywords: object orientation, optimization, Bayesian analysis
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