We consider Hamilton–Jacobi equations which characterize optimal controlled partial differential equations of the following types: the Allen–Cahn equation, the Cahn–Hilliard equation, a nonlinear Fokker–Planck equation, and aVlasov–Fokker–Planck equation. In each of the examples, the optimal control problem and its associated cost functional can be derived as limit from a microscopically define...

In this paper we consider Milner’s calculus CCS enriched by a probabilistic choice operator. The calculus is given operational semantics based on probabilistic transition systems. We define operational notions of preorder and equivalence as probabilistic extensions of the simulation preorder and the bisimulation equivalence respectively. We extend existing category-theoretic techniques for solv...

We consider classical solutions to the kinetic Fokker–Planck equation on a bounded domain $${\mathcal {O}} \subset ~{\mathbb {R}}^d$$ in position, and we obtain probabilistic representation of using Langevin diffusion process with absorbing boundary conditions phase-space cylindrical $$D = {\mathcal \times {\mathbb . Furthermore, Harnack inequality, as well maximum principle, are provided D for...

This paper revisits the notions of observer and diagnoser, and adapts them to probabilistic automata, in a setting of weighted automata computations. In the non stochastic case, observers and diagnosers are obtained by standard elementary steps, as state augmentation, epsilon-reduction and determinization. It is shown that these steps can be adapted to probabilistic automata, and algorithms to ...

An increasingly popular theory holds that the mind should be viewed as a near-optimal or rational engine of probabilistic inference, in domains as diverse as word learning, pragmatics, naive physics, and predictions of the future. We argue that this view, often identified with Bayesian models of inference, is markedly less promising than widely believed, and is undermined by post hoc practices ...