We apply the theory of metric-divergences between probability distributions and a variational approach in order to obtain a new model for probabilistic image segmentation. We study a specific model based on a very general measure between discrete probability distributions. We show experimentally that this model is competitive with some other models of the state of the art. In this work we use a...

Discrete choice models are commonly used by applied statisticians in numerous fields, such as marketing, economics, finance, and operations research. When agents in discrete choice models are assumed to have differing preferences, exact inference is often intractable. Markov chain Monte Carlo techniques make approximate inference possible, but the computational cost is prohibitive on the large ...

priority queues have a great importance in the study of computer communication networks in which different types of traffic require different quality of service standards. the discrete-time non-preemptive priority queuing model with priority jumps is proposed in this paper. on the basis of probability generating functions mean system contents and mean queuing delay characteristics are obtained....

The equilibrium position of an Euler-Bernoulli beam is investigated. Using the discrete Euler-Lagrange and Lagrange-d’Alembert principles, the behavior of the beam is simulated using variational integrators and, in particular, AVIs (Asynchronous Variational Integrators). Special emphasis is placed on the discrete mechanics and geometric structure underlying stress resultant beam models.

We present a receding horizon control scheme that utilizes a discrete time version of a nonlinear, iterative, projection-based optimization routine. The routine utilizes a projection operator to ensure feasible system trajectories at each iteration of the optimization resulting in valid control signals even when constraints on computation time prevent full convergence. An additional feature of ...

After presenting some basic ideas in the theory of large deviations, this paper applies the theory to a number of problems in statistical mechanics. These include deriving the form of the Gibbs state for a discrete ideal gas; describing probabilistically the phase transition in the Curie–Weiss model of a ferromagnet; and deriving variational formulas that describe the equilibrium macrostates in...

A brief introduction to discrete variational mechanics, motivated by the goal of constructing symplectic onestep methods, will be presented. Followed by the presentation of a new type of variational integrator that utilizes Taylor’s method in it’s construction. Time permitting, we will make some remarks on the pros/cons and stability of constructing the variational integrator from the Lagrangia...