Abstract
We are developing a computer application, called the Bayes Inference Engine, to provide the means to make inferences about models of physical reality within a Bayesian framework. The construction of complex nonlinear models is achieved by a fully object-oriented design. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical- programming environment. Maximum a posteriori solutions are achieved using a general, gradient-based optimization algorithm. The application incorporates a new technique of estimating and visualizing the uncertainties in specific aspects of the model.
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Hanson, K.M., Cunningham, G.S. (1996). The Bayes Inference Engine. In: Hanson, K.M., Silver, R.N. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 79. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5430-7_15
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DOI: https://doi.org/10.1007/978-94-011-5430-7_15
Publisher Name: Springer, Dordrecht
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