In this paper, the long-time behavior of Cesaro mean fundamental solution for fractional Heat equation corresponding to random time changes in Brownian motion is studied. We consider both stable subordinators leading equations with Caputo-Djrbashian derivative and more general cases differential-convolution operators, particular, distributed order derivatives.

We consider a multiclass GI|G|1 queueing system, operating under an arbitrary work-conserving scheduling policy π. We derive an invariance relation for the Cesaro sums of waiting times under π, which does not require the existence of limits of the Cesaro sums. This allows us to include in the set of admissible policies important classes, such as time-dependent and adaptive policies. For these c...

Since the turn of the century there have been several notions of convergence for subsets of metric spaces appear in the literature. Appearing in as a subset of these notions is the concepts of epi-convergence. In this paper we peresent definitions of epi-Cesaro convergence for sequences of lower semicontinuous functions from $X$ to $[-infty,infty]$ and Kuratowski Cesaro convergence of sequence...

In this study, we introduce the notions of Cesaro, strongly Ces`aro and statistical derivatives for real valued functions. These are based on concepts Cesaro convergence a sequence. Then establish some relationships between derivative derivative.

This paper considers several aspects of the sequential prediction problem for unbounded, non-stationary processes under p-th power loss lp(u, v) = |u−v|, 1 < p < ∞. In the first part of the paper it is shown that Bayes prediction schemes are Cesaro optimal under general conditions, that Cesaro optimal prediction schemes are unique in a natural sense, and that Cesaro optimality is equivalent to ...

We exhibit the analog of entropy map for multivariate Gaussian distributions on local fields. As in real case, image this lies supermodular cone and it determines distribution valuation vector. In general, can be defined non-archimedian valued fields whose group is an additive subgroup line, remains supermodular. also explicitly compute dimension 3.

We consider the problem of variational inference in probabilistic models with both log-submodular and log-supermodular higher-order potentials. These models can represent arbitrary distributions over binary variables, and thus generalize the commonly used pairwise Markov random fields and models with log-supermodular potentials only, for which efficient approximate inference algorithms are know...

We study cooperative games with supermodular costs. We show that supermodular costs arise in a variety of situations; in particular, we show that the problem of minimizing a linear function over a supermodular polyhedron—a problem that often arises in combinatorial optimization—has supermodular optimal costs. In addition, we examine the computational complexity of the least core and least core ...