Abstract Compositional synthesis relies on the discovery of assumptions, i.e., restrictions behavior remainder system that allow a component to realize its specification. In order avoid losing valid solutions, these assumptions should be necessary conditions for realizability. However, because there are typically many different behaviors same specification, behavioral often do not exist. this p...

We present a framework for Nesterov’s accelerated gradient flows in probability space to design efficient mean-field Markov chain Monte Carlo algorithms Bayesian inverse problems. Here four examples of information metrics are considered, including Fisher-Rao metric, Wasserstein-2 Kalman-Wasserstein metric and Stein metric. For both metrics, we prove convergence properties flows. In implementati...