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512.5 --- 512.622 --- 512.622 Polynomials, including binomials and prime factors --- Polynomials, including binomials and prime factors --- 512.5 General algebra --- General algebra --- Algebra --- Mathematics --- Mathematical analysis --- Equations, Theory of --- Équations algébriques --- Galois theory --- Galois, Théorie de --- Algèbre --- Groupes, Théorie des --- Group theory --- Équations algébriques --- Galois, Théorie de --- Algèbre

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Environmental protection. Environmental technology --- Landscape architecture --- Land use --- Architecture du paysage --- Utilisation du sol --- Periodicals. --- Planning --- Management --- Périodiques --- Planification --- Gestion --- Landschapsarchitectuur ; tijdschriften --- Agriculture Sciences --- Arts and Humanities --- Environmental Sciences --- Horticulture and Plant Culture (including Arboriculture) --- Performing Arts, Travel and Leisure --- General and Others --- Landscape architecture. --- Management. --- Planning. --- United States.

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574.5 --- Marine ecology --- 504.4 --- Biological oceanography --- Marine ecosystems --- Ocean --- Aquatic ecology --- 574.5 Hydrobiology. Aquatic biocoenoses and ecosystems. Food chains --- Hydrobiology. Aquatic biocoenoses and ecosystems. Food chains --- Ocean and sea environment. Including : sea floor, sea water --- Ecology --- Ecologie --- MARINE ECOLOGY --- TEXTBOOKS

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Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader. Christian P. Robert is Professor of Statistics at Université Paris Dauphine, and Head of the Statistics Laboratory of CREST, both in Paris, France. He has authored more than 150 papers in applied probability, Bayesian statistics and simulation methods. He is a fellow of the Institute of Mathematical Statistics and the recipient of an IMS Medallion. He has authored eight other books, including The Bayesian Choice which received the ISBA DeGroot Prize in 2004, Monte Carlo Statistical Methods with George Casella, and Bayesian Core with Jean-Michel Marin. He has served as Joint Editor of the Journal of the Royal Statistical Society Series B, as well as an associate editor for most major statistical journals, and was the 2008 ISBA President. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in both theoretical and applied statistics, is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and a Foreign Member of the Spanish Royal Academy of Sciences. He has served as Theory and Methods Editor of the Journal of the American Statistical Association, as Executive Editor of Statistical Science, and as Joint Editor of the Journal of the Royal Statistical Society Series B. In addition to books with Christian Robert, he has written Variance Components, 1992, with S.R. Searle and C.E. McCulloch; Statistical Inference, Second Edition, 2001, with Roger Berger; and Theory of Point Estimation, Second Edition, 1998, with Erich Lehmann. His latest book is Statistical Design 2008.

Mathematical statistics --- Monte Carlo method --- R (Computer program language) --- Markov processes --- Mathematical Computing --- Computer programs --- Data processing --- EPUB-LIV-FT LIVMATHE LIVSTATI SPRINGER-B --- 519.5 --- 519.245 --- 519.2 --- 681.3*G3 --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- 519.245 Stochastic approximation. Monte Carlo methods --- Stochastic approximation. Monte Carlo methods --- GNU-S (Computer program language) --- Domain-specific programming languages --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes --- Monte Carlo method - Computer programs --- Mathematical statistics - Data processing

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The recent research domain of asteroseismology studies the internal structure of stars, which is not directly observable, through the interpretation of the frequency spectra of stellar oscillations. The basic principles of asteroseismology are very similar to those developed by earth seismologists. Stellar interiors can be probed from oscillations because different oscillation modes penetrate to different depths inside the star. Asteroseismology is the only available method to derive the internal structure of the stars with high precision.This book - the first on asteroseismology - offers a practical guide for graduate students and scientists working in stellar astrophysics. It provides a general introduction to asteroseismology and comprehensive coverage of all its aspects: fundamental theory, observations and observational techniques, methodology of data analysis and seismic interpretations of various classes of multi-periodic pulsating stars.

Physics. --- Astronomy, Astrophysics and Cosmology. --- Astronomy. --- Physique --- Astronomie --- Astroseismology. --- Astroseismology --- Astronomy & Astrophysics --- Astrophysics --- Astronomy - General --- Physical Sciences & Mathematics --- Astero-seismology --- Asteroseismology --- Astérosismologie. --- Gravitation. --- Astrophysics. --- Cosmology. --- Space sciences. --- Astrophysics and Astroparticles. --- Extraterrestrial Physics, Space Sciences. --- Classical and Quantum Gravitation, Relativity Theory. --- Physical sciences --- Space sciences --- Extraterrestrial seismology --- Stellar oscillations --- Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics). --- Astronomical physics --- Astronomy --- Cosmic physics --- Physics --- Field theory (Physics) --- Matter --- Antigravity --- Centrifugal force --- Relativity (Physics) --- Science and space --- Space research --- Cosmology --- Science --- Properties