Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of non-exchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables. We compare their theoretical clustering properties with those of the Dirichlet Process and the two parameters Poisson-Dirichlet process. The proposed construction provides a complete characterization of the joint process, differently from existing work. We then propose the use of such process as prior distribution in a hierarchical Bayes modeling framework, and we describe a Markov Chain Monte Carlo sampler for posterior inference. We evaluate the performance of the prior and the robustness of the resulting inference in a simulation study, providing a comparison with popular Dirichlet Processes mixtures and Hidden Markov Models. Finally, we develop an application to the detection of chromosomal aberrations in breast cancer by leveraging array CGH data.
The use of Fault Tolerant Control (FTC) strategies provides alternatives for control loops in presence of faults by exploring physical and analytical redundancies available in the process. Therefore, the use of FTC techniques is essential to adequate industrial control systems concerning availability and reliability while preserving the closed-loop system performance. In this paper, we propose the extension of the fault-hiding approach for the control reconfiguration to a dynamic optimization problem in order to incorporate system constraints. This method uses the concepts of the virtual actuator and a moving horizon framework extending the original control reconfiguration problem to a quadratic programming problem. A multivariable neutralization process subjected to communication loss between the master controller and the final control elements is used as an example to analyze and demonstrate the performance of the proposed approach.
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