Lorek Pawel (13 results)
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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£ 73.29
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- Hardcover
Seller: California Books, Miami, FL, U.S.A.California Books
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£ 75.51
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- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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£ 73.91
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Condition: As New. Unread book in perfect condition.
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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£ 72.60
£ 15.00 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Condition: As New. Unread book in perfect condition.
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
Contact seller5-star sellerCondition: New
£ 71.98
£ 15.00 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Condition: New.
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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£ 95.02
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Condition: New.
- Hardcover
Seller: Revaluation Books, Exeter, , United KingdomRevaluation Books
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£ 101.87
£ 15.00 shippingShips from United Kingdom to U.S.A.Quantity: 2 available
Hardcover. Condition: Brand New. 590 pages. 9.26x6.11x9.25 inches. In Stock.
- Hardcover
Seller: moluna, Greven, , Germanymoluna
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£ 81.65
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Condition: New.
- Hardcover
- Print on Demand
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contact seller5-star sellerCondition: New
£ 75.35
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Hardcover. Condition: new. Hardcover. This book presents a broad range of computational techniques based on repeated random sampling, widely known as Monte Carlo methods and sometimes as stochastic simulation. These methods bring together ideas from probability theory, statistics, computer science, and statistical physics, provi…ding tools for solving problems in fields such as operations research, biotechnology, and finance.Topics include the generation and analysis of pseudorandom numbers (which are intended to imitate truly random numbers on a computer), the design and justification of Monte Carlo algorithms, and advanced approaches such as Markov chain Monte Carlo and stochastic optimization. In contrast to deterministic numerical methods, the outcome of a Monte Carlo algorithm is itself random and one needs the tools of probability and statistics to interpret these results meaningfully. The theoretical foundations, particularly the law of large numbers and central limit theorem, are combined with practical algorithms that reveal both the strengths and subtleties of stochastic simulation.The book includes numerous exercises, both theoretical and computational. Each chapter features step-by-step algorithms, illustrated examples, and results presented through numerical computations, tables, and a variety of plots and figures. All Python code used to produce these results is publicly available, allowing readers to reproduce and explore simulations on their own.Intended primarily for graduate students and researchers, the exposition focuses on core concepts and intuitive understanding, avoiding excessive formalism. The book is suitable both for self-study and as a course text and offers a clear pathway from foundational principles to modern applications. line-height: normal;">Topics include the generation and analysis of pseudorandom numbers (which are intended to imitate truly random numbers on a computer), the design and justification of Monte Carlo algorithms, and advanced approaches such as Markov chain Monte Carlo and stochastic optimization. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
- Print on Demand
Seller: Majestic Books, Hounslow, , United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 95.20
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand.
- Hardcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 97.08
£ 8.63 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND.
- Hardcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
£ 71.99
£ 37.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Hardcover. Condition: new. Hardcover. This book presents a broad range of computational techniques based on repeated random sampling, widely known as Monte Carlo methods and sometimes as stochastic simulation. These methods bring together ideas from probability theory, statistics, computer science, and statistical physics, provi…ding tools for solving problems in fields such as operations research, biotechnology, and finance.Topics include the generation and analysis of pseudorandom numbers (which are intended to imitate truly random numbers on a computer), the design and justification of Monte Carlo algorithms, and advanced approaches such as Markov chain Monte Carlo and stochastic optimization. In contrast to deterministic numerical methods, the outcome of a Monte Carlo algorithm is itself random and one needs the tools of probability and statistics to interpret these results meaningfully. The theoretical foundations, particularly the law of large numbers and central limit theorem, are combined with practical algorithms that reveal both the strengths and subtleties of stochastic simulation.The book includes numerous exercises, both theoretical and computational. Each chapter features step-by-step algorithms, illustrated examples, and results presented through numerical computations, tables, and a variety of plots and figures. All Python code used to produce these results is publicly available, allowing readers to reproduce and explore simulations on their own.Intended primarily for graduate students and researchers, the exposition focuses on core concepts and intuitive understanding, avoiding excessive formalism. The book is suitable both for self-study and as a course text and offers a clear pathway from foundational principles to modern applications. line-height: normal;">Topics include the generation and analysis of pseudorandom numbers (which are intended to imitate truly random numbers on a computer), the design and justification of Monte Carlo algorithms, and advanced approaches such as Markov chain Monte Carlo and stochastic optimization. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Hardcover
- Print on Demand
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
£ 101.23
£ 27.96 shippingShips from Australia to U.S.A.Quantity: 1 available
Hardcover. Condition: new. Hardcover. This book presents a broad range of computational techniques based on repeated random sampling, widely known as Monte Carlo methods and sometimes as stochastic simulation. These methods bring together ideas from probability theory, statistics, computer science, and statistical physics, provi…ding tools for solving problems in fields such as operations research, biotechnology, and finance.Topics include the generation and analysis of pseudorandom numbers (which are intended to imitate truly random numbers on a computer), the design and justification of Monte Carlo algorithms, and advanced approaches such as Markov chain Monte Carlo and stochastic optimization. In contrast to deterministic numerical methods, the outcome of a Monte Carlo algorithm is itself random and one needs the tools of probability and statistics to interpret these results meaningfully. The theoretical foundations, particularly the law of large numbers and central limit theorem, are combined with practical algorithms that reveal both the strengths and subtleties of stochastic simulation.The book includes numerous exercises, both theoretical and computational. Each chapter features step-by-step algorithms, illustrated examples, and results presented through numerical computations, tables, and a variety of plots and figures. All Python code used to produce these results is publicly available, allowing readers to reproduce and explore simulations on their own.Intended primarily for graduate students and researchers, the exposition focuses on core concepts and intuitive understanding, avoiding excessive formalism. The book is suitable both for self-study and as a course text and offers a clear pathway from foundational principles to modern applications. line-height: normal;">Topics include the generation and analysis of pseudorandom numbers (which are intended to imitate truly random numbers on a computer), the design and justification of Monte Carlo algorithms, and advanced approaches such as Markov chain Monte Carlo and stochastic optimization. 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.
