Estimation, learning, pattern recognition, diagnostics, fault detection and adaptive control are prominent examples of dynamic decision making under uncertainty. Under rather general conditions, they can be cast into a common theoretical framework labelled as Bayesian decision making. Richness of the practically developed variants stems from: (i)… (More)
The paper shares experience gained in application of dynamic Bayesian approach to control problems in the field of metal rolling. The contribution introduces basic notions of theory applied and provides the algorithmic as well as application-oriented solutions developed. Specifically, the consistent use of the approach resulted in an advanced decision… (More)
Paper formulates the problem of multiobjective probabilistic mixture control design and proposes its general solution with both system model and target represented by finite probabilistic mixtures. A complete feasible algorithmic solution for mixtures with components formed by normal auto-regression models with external variable is provided.
— Any systematic decision-making design selects a decision strategy that makes the resulting closed-loop behaviour close to the desired one. Fully Probabilistic Design (FPD) describes modelled and desired closed-loop behaviours via their distributions. The designed strategy is a minimiser of Kullback-Leibler divergence of these distributions. FPD: i)… (More)