The Asymptotically Unbiased Prior Distribution

Unbiased Finite Planar Graphs are Asymptotically Recurrent

Suppose that G j is a sequence of finite connected planar graphs, and in each G j a special vertex, called the root, is chosen randomly-uniformly. We introduce the notion of a distributional limit G of such graphs. Assume that the vertex degrees of the vertices in G j are bounded, and the bound does not depend on j. Then after passing to a subsequence, the limit exists, and is a random rooted g...

Asymptotically Unbiased Average Consensus under Measurement Noises and Fixed Topologies

This paper is concerned with average-consensus control under directed topologies and random measurement noises. To attenuate the measurement noises, time-varying consensus gains are introduced in the protocol. It is shown that under the protocol designed, all agents’ states converge to a common Gaussian random variable, whose mathematical expectation is just the average of the initial states, a...

0 Unbiased Finite Planar Graphs are Asymptotically Recurrent

Suppose that G j is a sequence of finite connected planar graphs, and in each G j a special vertex, called the root, is chosen randomly-uniformly. We introduce the notion of a distributional limit G of such graphs. Assume that the vertex degrees of the vertices in G j are bounded, and the bound does not depend on j. Then after passing to a subsequence, the limit exists, and is a random rooted g...

Prior distribution

An Unbiased Second-Order Prior for High-Accuracy Motion Estimation

Virtually all variational methods for motion estimation regularize the gradient of the flow field, which introduces a bias towards piecewise constant motions in weakly textured areas. We propose a novel regularization approach, based on decorrelated second-order derivatives, that does not suffer from this shortcoming. We then derive an efficient numerical scheme to solve the new model using pro...