Isbn: 9789811329708 - Monte Carlo Methods (9 results)

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    • Language: English

      Published by Springer, 2020

      9811329702 / 9789811329708

      • Hardcover

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    • Language: English

      Published by Springer, 2020

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    • Language: English

      Published by Springer, 2020

      9811329702 / 9789811329708

      • Hardcover

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      Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.

    • Language: English

      Published by Springer, 2020

      9811329702 / 9789811329708

      • Hardcover
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      Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand

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    • Language: English

      Published by Springer, Springer Feb 2020, 2020

      9811329702 / 9789811329708

      • Hardcover
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      Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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      Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research. 440 pp. Englisch.

    • Language: English

      Published by Springer Singapore, 2020

      9811329702 / 9789811329708

      • Hardcover
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      Seller: moluna, Greven, Germanymoluna

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      Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A Monte Carlo textbook suitable for students and researchers in the areas of computer vision, machine learning, robotics, artificial intelligence, graphics, etcAn easy to understand textbook featuring a wealth of sample applications.

    • Language: English

      Published by Springer, 2020

      9811329702 / 9789811329708

      • Hardcover
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      Seller: Majestic Books, Hounslow, United KingdomMajestic Books

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    • Language: English

      Published by Springer, 2020

      9811329702 / 9789811329708

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      Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios

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    • Language: English

      Published by Springer, Springer Feb 2020, 2020

      9811329702 / 9789811329708

      • Hardcover
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      Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000

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      Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 440 pp. Englisch.