Titlebook: New Directions in Statistical Physics; Econophysics, Bioinf Luc T. Wille Book 2004 Springer-Verlag Berlin Heidelberg 2004 Diffusion.Econoph

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Predicting the Direction of a Time Seriesed. Finally, the method is applied to two illustrative time series: the daily closing prices of the S&P500 market index (for the period 1995–2001) and the quarterly growth rates for the US gross domestic product from 1959 till 2000. The results obtained for these two cases are extremely encouraging.

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First- and Last-Passage Algorithms in Diffusion Monte Carlohod; the use of the Feynman-Kac formula, combined with GFFP to actually perform path integrals, one patch at a time; and the development of last-passage diffusion methods; these drastically improve the efficiency of diffusion Monte Carlo methods. All of these techniques are described in detail, with specific examples.

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Privacy and Data Exchangesng them again depending on the answers given, without revealing their identity or answers. After a brief tutorial on public key cryptography, solutions to both of these problems are provided and discussed.

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Statistical Physics and the Clustering Problemlustering which performs a non-parametric clas-sification without any assumptions on the distribution of clusters or the number of classes. The technique is illustrated on a biological example (reconstruction of phylogenetic trees) and one from coding theory. The merits of various approaches and the remaining challenges are also discussed.

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Book 2004ications to thermodynamics, magnetic phase transitions, and order/disorder transformations, to name just a few. However, the tools of statistical physics can be profitably used to investigate any system with a large number of components. Thus, recent years have seen these methods applied in many une

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eller potential..It provides a new view.Includes supplementaStatistical physics addresses the study and understanding of systems with many degrees of freedom. As such it has a rich and varied history, with applications to thermodynamics, magnetic phase transitions, and order/disorder transformations

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Toward an Understanding of Financial Markets using Multi-agent Gamesblack-box time-series, and is then run into the future to extract next-step and multi-step predictions. Such predictions have potential use as the basis for improved risk management and portfolio optimization strategies.