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c Societ` a Italiana di Fisica / Springer-Verlag 2008 The 11th Workshop on The Physics of Excited Nucleons, NSTAR 2007, was held at the University of Bonn, Germany,fromSeptember5–8,2007.ItwasthelatestofaseriesofsuccessfulconferencesattheRensselaerPolytechnic Institute (1988), Florida State University (1994 and 2005), Je?erson Lab (1995 and 2000), INT Seattle (1996), GWU ? Washington (1997), ECT Trento (1998), Mainz (2001), Pittsburgh (2002) and the LPSC Grenoble (2004). A Baryon Resonance Analysis Group (BRAG) meeting immediately before the workshop focused especially on the physical meaning of bare and dressed scattering matrix singularities. A focus workshop on? photoproduction rounded o? the NSTAR 2007. The goal of NSTAR 2007 was to bring together experts on all areas of physics relevant to baryon spectroscopy, both in experiment and theory. Latest results were presented in 30 plenary talks and 34 parallel contributions, the proceedings of which are collected in this volume. The workshop was attended by 123 scientists of 41 universities and laboratories from 16 countries. Exciting new high-precision data were shown from facilities in Asia, the US and Europe, e.g. BES, BNL, COSY, ELSA, GRAAL, JLab, MAMI and LEPS. Large-acceptance detectors provide complete angular distributions in many reaction channels. Particular emphasis is put on the measurement of single and double polarisation observables such that many new polarization measurements can be expected in forthcoming meetings.
A co-publication of the AMS and the Courant Institute of Mathematical Sciences at New York University This book is a concise and self-contained introduction of recent techniques to prove local spectral universality for large random matrices. Random matrix theory is a fast expanding research area, and this book mainly focuses on the methods that the authors participated in developing over the past few years. Many other interesting topics are not included, and neither are several new developments within the framework of these methods. The authors have chosen instead to present key concepts that they believe are the core of these methods and should be relevant for future applications. They keep technicalities to a minimum to make the book accessible to graduate students. With this in mind, they include in this book the basic notions and tools for high-dimensional analysis, such as large deviation, entropy, Dirichlet form, and the logarithmic Sobolev inequality. This manuscript has been developed and continuously improved over the last five years. The authors have taught this material in several regular graduate courses at Harvard, Munich, and Vienna, in addition to various summer schools and short courses. Titles in this series are co-published with the Courant Institute of Mathematical Sciences at New York University.
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.