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Book Description
Les travaux présentés portent sur l'évaluation de prévisions météorologiques probabilistes issues de systèmes de prévision d'ensemble. Les principaux critères utilisés sont les termes de résolution et de fiabilité du score de Brier. Les prévisions issues de systèmes opérationnels sont comparées à celles obtenues par des méthodes statistiques à partir de l'intégration unique d'un modèle de prévision. On s'intéresse également à des systèmes de prévision d'ensemble consistant à regrouper les prévisions issues de quelques centres opérationnels. Les conditions requises pour une estimation réaliste de la performance de prévisions issues d'un ensemble sont examinées par ailleurs. La variabilité spatiale et temporelle de la fiabilité impose une stratification des données que ne permettent pas toujours les échantillons de taille réduite disponibles pour la vérification. Un autre problème essentiel est celui de la catégorisation des probabilités prévues, qui permet la décomposition du score de Brier.
Book Description
Les travaux présentés portent sur l'évaluation de prévisions météorologiques probabilistes issues de systèmes de prévision d'ensemble. Les principaux critères utilisés sont les termes de résolution et de fiabilité du score de Brier. Les prévisions issues de systèmes opérationnels sont comparées à celles obtenues par des méthodes statistiques à partir de l'intégration unique d'un modèle de prévision. On s'intéresse également à des systèmes de prévision d'ensemble consistant à regrouper les prévisions issues de quelques centres opérationnels. Les conditions requises pour une estimation réaliste de la performance de prévisions issues d'un ensemble sont examinées par ailleurs. La variabilité spatiale et temporelle de la fiabilité impose une stratification des données que ne permettent pas toujours les échantillons de taille réduite disponibles pour la vérification. Un autre problème essentiel est celui de la catégorisation des probabilités prévues, qui permet la décomposition du score de Brier.
Author: Philippe Gourbesville Publisher: Springer ISBN: 9811072183 Category : Science Languages : en Pages : 1205
Book Description
This book gathers a collection of extended papers based on presentations given during the SimHydro 2017 conference, held in Sophia Antipolis, Nice, France on June 14–16, 2017. It focuses on how to choose the right model in applied hydraulics and considers various aspects, including the modeling and simulation of fast hydraulic transients, 3D modeling, uncertainties and multiphase flows. The book explores both limitations and performance of current models and presents the latest developments in new numerical schemes, high-performance computing, multiphysics and multiscale methods, and better interaction with field or scale model data. It gathers the lastest theoretical and innovative developments in the modeling field and presents some of the most advance applications on various water related topics like uncertainties, flood simulation and complex hydraulic applications. Given its breadth of coverage, it addresses the needs and interests of practitioners, stakeholders, researchers and engineers alike.
Author: Hans U. Gerber Publisher: Springer Science & Business Media ISBN: 3662026554 Category : Mathematics Languages : en Pages : 137
Book Description
HaIley's Comet has been prominently displayed in many newspapers during the last few months. For the first time in 76 years it appeared this winter, clearly visible against the nocturnal sky. This is an appropriate occasion to point out the fact that Sir Edmund Halley also constructed the world's first life table in 1693, thus creating the scientific foundation of life insurance. Halley's life table and its successors were viewed as deterministic laws, i. e. the number of deaths in any given group and year was considered to be a weIl defined number that could be calculated by means of a life table. However, in reality this number is random. Thus any mathematical treatment of life insurance will have to rely more and more on prob ability theory. By sponsoring this monograph the Swiss Association of Actuaries wishes to support the "modern" probabilistic view oflife contingencies. We are fortu nate that Professor Gerber, an internationally renowned expert, has assumed the task of writing the monograph. We thank the Springer-Verlag and hope that this monograph will be the first in a successful series of actuarial texts. Hans Bühlmann Zürich, March 1986 President Swiss Association of Actuaries Preface Two major developments have influenced the environment of actuarial math ematics. One is the arrival of powerful and affordable computers; the once important problem of numerical calculation has become almost trivial in many instances.
Author: Michael J. Kearns Publisher: MIT Press ISBN: 9780262111935 Category : Computers Languages : en Pages : 230
Book Description
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
Author: Vladimir Vapnik Publisher: Springer Science & Business Media ISBN: 1475732643 Category : Mathematics Languages : en Pages : 324
Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Author: CIKM 13 Conference Committee Publisher: ISBN: 9781450326964 Category : Computers Languages : en Pages : 938
Book Description
CIKM'13: 22nd ACM International Conference on Information and Knowledge Management Oct 27, 2013-Nov 01, 2013 San Francisco, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.
Author: Eric P. Chassignet Publisher: Springer Science & Business Media ISBN: 9781402039812 Category : Science Languages : en Pages : 600
Book Description
This volume covers a wide range of topics and summarizes our present knowledge in ocean modeling, ocean observing systems, and data assimilation. The Global Ocean Data Assimilation Experiment (GODAE) provides a framework for these efforts: a global system of observations, communications, modeling, and assimilation that will deliver regular, comprehensive information on the state of the oceans, engendering wide utility and availability for maximum benefit to the community.
Author: Mehrez Zribi Publisher: Elsevier ISBN: 0128180862 Category : Science Languages : en Pages : 352
Book Description
Water Resources in the Mediterranean Region summarizes and collates scientific developments around water resources in the Mediterranean socio-economic environment through a multidisciplinary framework synthesizing hydrology, hydrogeology, climate, bioclimatology, economics, and geography. As such, it provides essential information for any reader looking to learn more about the Mediterranean which is experiencing the impact of climate change and concurrent complex issues of anthropogenic effects, especially in agriculture and other resource uses. Water Resources in the Mediterranean Region covers different challenges in the issue of the evolution of water resources in the Mediterranean. It is intended for PhD students, research scientists, and managers interested in new solutions and approaches for water management and in the forecast of future water dynamics. Offers multidisciplinary content providing global visions of the challenges faced in the Mediterranean region Presents fundamental and operational studies, providing the reader with information on how to implement these actions and results themselves Written in a pedagogical manner, allowing for ease of reading for both researchers and water managers
Author: Richard S. Sutton Publisher: MIT Press ISBN: 0262352702 Category : Computers Languages : en Pages : 549
Book Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Author: Frédéric Atger
Author: Frédéric Atger
Author: Philippe Gourbesville
Author: Neural Information Processing Systems Foundation
Author: Hans U. Gerber
Author: Michael J. Kearns
Author: Vladimir Vapnik
Author: CIKM 13 Conference Committee
Author: Eric P. Chassignet
Author: Mehrez Zribi
Author: Richard S. Sutton