In order to replace the univariate indicators standard in the literature (cp. [Opp96]) by a multivariate representation of business cycles, the relevant ’stylized facts’ are to be identified which optimally characterize the development of business cycle phases. Based on statistical classification methods we found that, somewhat surprisingly, only two variables, ’wage and salary earners’ and ’un...

in the present study, fe3o4 super paramagnetic nanoparticles were synthesized via co-precipitation of fe3+ and fe2+ chloride salts in the presence of ammonia solution. the product was characterized by fourier transform infrared spectroscopy (ft-ir), transmission electron microscopy (tem), and zeta–potential particle size distributing methods. the synthetic procedure of nano iron oxides were opt...

background: hydrocarbons degradation is principally achieved by microorganisms in natural environments. the extent of hydrocarbons biodegradation is mainly conditioned by environmental factors and its success depends on the optimal condition for the crude oil degrading isolates. objectives: the aims of the current study was to isolate and identify crude oil degrading bacterium from surface sedi...

Identifying convergence in numerical optimization is an ever-present, difficult, and often subjective task. The statistical framework of Gaussian process surrogate model optimization provides useful measures for tracking optimization progress; however, the identification of convergence via these criteria has often provided only limited success and often requires a more subjective analysis. Here...

Optimization has always played a central role in machine learning and advances in the field of optimization and mathematical programming have greatly influenced machine learning models. However the connection between optimization and learning is much deeper : one can phrase statistical and online learning problems directly as corresponding optimization problems. In this dissertation I take this...

This paper reviews methods for computer assisted medical intervention using statistical models and machine learning technologies, which would be particularly useful for representing prior information of anatomical shape, motion, and deformation to extrapolate intraoperative sparse data as well as surgeons’ expertise and pathology to optimize interventions. Firstly, we present a review of method...

Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima and the failure of the analysis to return a value to the optimizer. One possible remedy to alleviate these issues is to use surrogate models in lieu of the computational models or simulations and de...

Dynamic stochastic optimization techniques are highly relevant for applications in electricity production and trading since there are uncertainty factors at different time stages (e.g., demand, spot prices) that can be described reasonably by statistical models. In this paper, two aspects of this approach are highlighted: scenario tree approximation and risk aversion. The former is a procedure ...