Recently, bayesian probability theory has been used at a number of experiments to fold uncertainties and interdependencies in the diagnostic data and forward models, together with prior knowledge of the state of the plasma, to increase accuracy of inferred physics variables. A new probabilistic framework, MINERVA, based on bayesian graphical models, has been used at JET and W7-AS to yield predi...

We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum Bayesian inference wherein one considers mixed quantum states rather than probability distributions as representative of degrees of belief. The diagrammatic framework is stated in the graphical language of symmetric mo...

A recently proposed Bayesian modeling framework for classification facilitates both the analysis and optimization of error estimation performance. The Bayesian error estimator is then defined to have optimal mean-square error performance, but in many situations closed-form representations are unavailable and approximations may not be feasible. To address this, we present a method to optimally c...

Bayesian decision theory is a mathematical framework that models reasoning and decision-making under uncertainty. Around 1990, perceptual psychologists began constructing detailed Bayesian models of perception. 1 This research program has proved enormously fruitful. As two leading perceptual psychologists put it, “Bayesian concepts are transforming perception research by providing a rigorous ma...