Suchergebnisse

G.B. acknowledges the financial support provided by the State Education Development Agency for project No.1.1.1.2/VIAA/3/19/444 (agreement No. 1.1.1.2/16/I/001) realized at the Institute of Solid State Physics, University of Latvia. A.K. and A.K. would like to thank the support of the Latvian Council of Science project No. lzp-2019/1-0071. Institute of Solid State Physics, University of Latvia as the Center of Excellence has received funding from the European Union's Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART2. ; The existence of exchange-coupled Ni ions – the so-called magnetic dimers – in wolframite-type NiWO and ZnNiWO solid solutions with high nickel content was discovered by X-ray absorption spectroscopy combined with reverse Monte Carlo (RMC) simulations. Temperature- (10–300 K) and composition-dependent X-ray absorption spectra were measured at the Ni K-edge, Zn K-edge, and W L-edge of microcrystalline NiWO, ZnNiWO and ZnWO. Structural models were obtained from simultaneous analysis of the extended X-ray absorption fine structure (EXAFS) spectra at three metal absorption edges using RMC simulations. The obtained radial distribution functions for different atomic pairs made it possible to trace in detail the changes in the local environment of metal ions and the effect of thermal disorder. Dimerization of Ni ions within quasi-one-dimensional zigzag chains of [NiO] octahedra was evidenced in NiWO in the whole studied temperature range. It manifests itself as the splitting of the Ni–Ni radial distribution function into two separate peaks. The effect is further preserved in solid solutions ZnNiWO for , which is related to the probability to find two Ni ions in neighbouring positions. --- / / / --- This is the preprint version of the following article: Georgijs Bakradze, Aleksandr Kalinko, Alexei Kuzmin,Evidence of nickel ions dimerization in NiWO and NiWO-ZnWO solid solutions probed by EXAFS spectroscopy and reverse Monte Carlo simulations, Acta Materialia, Volume 217, 117171 (2021), DOI https://doi.org/10.1016/j.actamat.2021.117171, which has been published in final form at https://www.sciencedirect.com/science/article/abs/pii/S1359645421005516, under the CC BY-NC-ND license. ; State Education Development Agency for project No.1.1.1.2/VIAA/3/19/444 (agreement No. 1.1.1.2/16/I/001); Latvian Council of Science project No. lzp-2019/1-0071; Institute of Solid State Physics, University of Latvia as the Center of Excellence has received funding from the European Union's Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART2.

Some basic theory of finance -- Basic Monte Carlo methods -- Variance reduction techniques -- Simulating the value of options -- Quasi-Monte Carlo multiple integration -- Estimation and calibration -- Sensitivity analysis, estimating derivatives and the Greeks -- Other methods and conclusions

This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems. It contains standard material usually considered in Monte Carlo simulation as well as new material such as variance reduction techniques, regenerative simulation, and Monte Carlo optimization.

This book can serve as the text for a one-semester course on Monte Carlo simulation. The intended audience is advanced undergraduate students or students on master's programs who wish to learn the basics of this exciting topic and its applications to finance. The book is largely self-contained. The only prerequisite is some experience with probability and statistics. Prior knowledge on option pricing is helpful but not essential. As in any study of Monte Carlo simulation, coding is an integral part and cannot be ignored. The book contains a large number of MATLAB coding exercises. They are designed in a progressive manner so that no prior experience with MATLAB is required. Much of the mathematics in the book is informal. For example, randomvariables are simply defined to be functions on the sample space, even though they should be measurable with respect to appropriate algebras; exchanging the order of integrations is carried out liberally, even though it should be justified by the Tonelli-Fubini Theorem. The motivation for doing so is to avoid the technical measure theoretic jargon, which is of little concern in practice and does not help much to further the understanding of the topic. The book is an extension of the lecture notes that I have developed for an undergraduate course on Monte Carlo simulation at Brown University. I would like to thank the students who have taken the course, as well as the Division of Applied Mathematics at Brown, for their support. Hui Wang Providence, Rhode Island January, 2012.