This paper presents a hierarchical framework for the identification of nonlinear hybrid systems in form Switched Nonlinear AutoRegressive models with eXogenous variables (SNARX). The is done via three levels inference, using Bayes' rule. In first level, model parameters are computed Maximum Posteriori (MAP) estimator. posterior distribution therein involved depends on hyper-parameters that tune...

We consider the problem of black-box multi-objective optimization (MOO) using expensive function evaluations (also referred to as experiments), where goal is approximate true Pareto set solutions by minimizing total resource cost experiments. For example, in hardware design optimization, we need find designs that trade-off performance, energy, and area overhead computational simulations. The ke...