Sinossi
This book covers key ideas and concepts. It is an ideal introduction for graduate students in any field where Bayesian data assimilation is applied.
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Informazioni sugli autori
Sebastian Reich is Professor of Numerical Analysis at the University of Potsdam (full time) and the University of Reading (part time). He also holds an honorary visiting professorship at Imperial College London. Reich is the author of over 100 journal articles and the co-author of Simulating Hamiltonian Dynamics (Cambridge, 2005), which has received more than 600 citations. His research areas cover numerical analysis and scientific computing with applications to classical mechanics, molecular dynamics, geophysical fluid dynamics, and data assimilation. In 2003 he received the Germund Dahlquist Prize from the Society for Industrial and Applied Mathematics (SIAM) for his work on geometric integration methods.
Colin Cotter has been a Senior Lecturer in the Department of Mathematics at Imperial College London since 2013. He has published more than 40 journal articles and three book chapters, on the design, analysis and implementation of numerical methods for numerical weather prediction, ocean forecasting and climate modelling; data assimilation; image registration; geometric mechanics and other topics in scientific computing and numerical analysis. His publications have been cited approximately 500 times. He is a key member of the Met Office/STFC/NERC-funded multi-institutional 'Gung-Ho' project which will design a next generation dynamical core for the UK weather prediction and climate forecasting system. He is also a co-investigator for the EPSRC Mathematics of Planet Earth Centre for Doctoral Training, and for the EPSRC Platform for Research in Simulation Methods (PRISM).
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Hardcover. Condizione: new. Hardcover. In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas. This book focuses on the Bayesian approach to data assimilation, outlining the subject's key ideas and concepts, and explaining how to implement specific data assimilation algorithms. It is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781107069398
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Hardcover. Condizione: new. Hardcover. In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas. This book focuses on the Bayesian approach to data assimilation, outlining the subject's key ideas and concepts, and explaining how to implement specific data assimilation algorithms. It is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9781107069398
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book focuses on the Bayesian approach to data assimilation, outlining the subject s key ideas and concepts, and explaining how to implement specific data assimilation algorithms. It is an ideal introduction for graduate students in applied mathematics,. Codice articolo 37868457
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Hardcover. Condizione: new. Hardcover. In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas. This book focuses on the Bayesian approach to data assimilation, outlining the subject's key ideas and concepts, and explaining how to implement specific data assimilation algorithms. It is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9781107069398
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Buch. Condizione: Neu. Probabilistic Forecasting and Bayesian Data Assimilation | Sebastian Reich (u. a.) | Buch | Gebunden | Englisch | 2015 | Cambridge University Press | EAN 9781107069398 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Codice articolo 104908852
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