Abstract Parameter estimation via unbinned maximum likelihood fits is central for many analyses performed in high energy physics. Unbinned using event weights, example to statistically subtract background contributions the sPlot formalism, or correct acceptance effects, have recently seen increasing use community. However, it well known that naive approach of parameter uncertainties second deri...

The composite likelihood (CL) is amongst the computational methods used for estimation of high-dimensional multivariate normal (MVN) copula models with discrete responses. Its advantage, as a surrogate method, that based on independence univariate marginal regression and non-regression parameters pairwise correlation parameters. Nevertheless, efficiency CL method estimating can be low. For resp...

AbstmctIn the literature the performance of joint maximum-likelihood sequence estimation for trellis-coded modulation systems was analyzed under the assumption that fading is so slow that the channel does not change during all error events. In this paper we extend the performance analysis to general fading processes by considering the correlation function of the time-variant channel impulse res...

In this paper we present a framework for unsupervised segmentation of white matter fiber traces obtained from diffusion weighted MRI data. Fiber traces are compared pairwise to create a weighted undirected graph which is partitioned into coherent sets using the normalized cut (N cut) criterion. A simple and yet effective method for pairwise comparison of fiber traces is presented which in combi...

This paper presents an iterative maximum likelihood framework for motion segmentation via the pairwise checking of pixel blocks. We commence from a characterisation of the motion blocks in terms of a matrix of pairwise similarity weghts for their motion vectors. The eigenvectors of this similarity weight matrix represent the initial pairwise clusters, i.e the independant motions present in the ...