Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm

In this section we construct an estimator that works well whenever the distribution of X is sufficiently spherical in the sense that a positive fraction of the eigenvalues of

More generally, for any L-function with a functional equation there is a relation between the leading and the sub-leading coefficient of the Taylor expansion of the L-function at

Bollob´ as and Riordan showed [3] that the expectation of the clustering coefficient of the model from [4] is asymptotically proportional to (log n) 2 /n.. Bollob´ as and Riordan

Discussion of the paper ”Rank- Normalization, Folding, and Localization: An Improved R b for Assessing Convergence of MCMC”.. Bayesian Analysis, International Society for

Despite these solutions are effective in producing a proper coloring, generally minimizing the number of colors, they produce highly skewed color classes, undesirable for

There was a breakthrough two years ago, when Baladi and Vall´ee [2] extended the previous method for obtaining limit distributions, for a large class of costs, the so-called

on the decision to delegate the task management to OpenMP, the task granularity is en- forced to reach high performance, while the task submission order is a consequence of the need

JACOD, Random sampling in estimation problems for continuous Gaussian processes with independent increments, Stoch. JEGANATHAN, On the asymptotic theory of estimation