We propose a smooth formulation of multiple-point statistics that enables us to solve inverse problems using gradient-based optimization techniques. We introduce a differentiable function that quantifies the mismatch between multiple-point statistics of a training image and of a given model. We show that, by minimizing this function, any continuous image can be gradually transformed into an ima...

Uncertainties such as measurement noise are unavoidable in inverse problems and often lead to unstable solutions due to the ill-posed nature of such problems. However, there is a rich statistical information contained in the actual data that is often not used. In this paper, we explore the solution of stochastic inverse heat conduction problems where the unknowns (e.g. boundary heat flux or dis...

Abstract In this paper, some predictive results of dual generalized order statistics (DGOSs) from the inverse Weibull distribution are obtained. For goal, different and reconstructive pivotal quantities proposed. Moreover, several intervals concerning DGOSs based on constructed. Furthermore, maximum likelihood predictor as well estimates studied. Finally, simulation studies carried out to asses...

Networks for the blind channel equalization can be designed as a combination of a prewhitening lter (adjusted by means of 2nd-order statistics) and an inverse all-pass system (adjusted by 4th-order statistics). In this paper an appropriate FIR approximation of inverse all-pass systems is presented. On the other hand the inverse all-pass modelling can be avoided if the minimum phase prewhitening...

We provide an overview on superstatistical techniques applied to complex systems with time scale separation. Three examples of recent applications are dealt with in somewhat more detail: the statistics of small-scale velocity differences in Lagrangian turbulence experiments, train delay statistics on the British rail network, and survival statistics of cancer patients once diagnosed with cancer...

Recently reclaimed importance of the empirical distribution function of the eigenvalues of Wishart matrices was attached to the analysis of MIMO systems in [1]. But for the analysis of MIMO systems with signal processing at the transmitter [2] it is necessary to know the empirical distribution function of the inverse eigenvalues and the first and second moment of these distributions. We extend ...

We propose a simulation algorithm to estimate means, variances, and covariances for a set of order statistics from inverse-Gaussian (IG) distributions. Given a set of Monte Carlo data, the algorithm estimates these values simultaneously. Two types of control variates are used: internal uniform and external exponential. Simulation results show that exponential control variates work better, best ...

Order statistics arising from inverse Weibull (IW) distribution are considered. Exact expression for the single moments of order statistics are derived. Also, the variances and covariances are calculated. Based on the moments of order statistics, the best linear unbiased estimates (BLUEs) for the location and scale parameters of IW distribution are obtained. In addition, these BLUEs are applied...

Investigations of inverse statistics (a concept borrowed from turbulence) in stock markets, exemplified with filtered Dow Jones Industrial Average, S&P 500, and NASDAQ, have uncovered a novel stylized fact that the distribution of exit time follows a power law p(τρ) ∼ ρ −α with α ≈ 1.5 at large τρ and the optimal investment horizon τ∗ ρ scales as ρ γ [1,2,3]. We have performed an extensive anal...