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Description Distribution estimation algorithms for abductive inference (total or partial) in dynamic domains. Structural learning of dynamic Bayesian networks with discrete and continuous variables (parametric
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Investigate the use of causal discovery methods in "concept drift" situations in structural causal models. In semiparametric Bayesian networks, investigate the selection of covariance matrices and the
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Prof. M. Fernando Gonzalez-Zalba, a multidisciplinary and dynamic research team passionate about building a scalable quantum computer based on silicon technology. Moreover, the PhD will have a strong
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subjects related to Mathematics for Economics, specifically in the Bachelor's Degree in Economics (subjects: Mathematics for Economics I, II, III, and IV, and Bayesian Methods). Additionally, the candidate
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parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing; additionally other methods such as simulation-based inference Good computing skills
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of parametrization of these models based on least squares and Bayesian calibration techniques employing longitudinal series of anonymized PSA data from patients. 3) Analysis of the predictions, parameters, and