Title | Monte Carlo and Quasi-Monte Carlo Methods 2002 [electronic resource] : Proceedings of a Conference held at the National University of Singapore, Republic of Singapore, November 25-28, 2002 / edited by Harald Niederreiter |
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Imprint | Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004 |
Connect to | http://dx.doi.org/10.1007/978-3-642-18743-8 |
Descript | XX, 460 p. 27 illus. online resource |
Invited Papers -- Finance: A Fertile Field for Applications of MC and QMC -- How Many Random Bits Do We Need for Monte Carlo Integration? -- On Tractability of Weighted Integration for Certain Banach Spaces of Functions -- Polynomial Integration Lattices -- Approximate Bayesian Computation and MCMC -- New Challenges for the Simulation of Stochastic Processes -- Stochastic Models and Monte Carlo Algorithms for Boltzmann Type Equations -- Digital Nets, Duality, and Algebraic Curves -- Contributed Papers -- Generalized Mersenne Prime Number and Its Application to Random Number Generation -- Constructing Good Lattice Rules with Millions of Points -- Lattice Structure of Nonlinear Pseudorandom Number Generators in Parts of the Period -- Simulation for American Options: Regression Now or Regression Later? -- Perturbation Monte Carlo Methods for the Solution of Inverse Problems -- Quantum Boolean Summation with Repetitions in the Worst-Average Setting -- The Strong Tractability of Multivariate Integration Using Lattice Rules -- Minimizing Effective Dimension Using Linear Transformation -- Component by Component Construction of Rank-1 Lattice Rules Having O(n-1(ln(n))d) Star Discrepancy -- Stratification by Rank-1 Lattices -- Walsh Series Analysis of the Star Discrepancy of Digital Nets and Sequences -- Quasi-Monte Carlo Methods for Estimating Transient Measures of Discrete Time Markov Chains -- Quasi-Monte Carlo Methods for Elliptic BVPs -- Stable Connectivity of Networks and Its Monte Carlo Estimation -- Random Number Generators Based on Linear Recurrences in % MathType!MTEF!2!1!+- % feaagaart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbWexLMBb50ujbqegm0B % 1jxALjharqqr1ngBPrgifHhDYfgasaacH8srps0lbbf9q8WrFfeuY- % Hhbbf9v8qqaqFr0xc9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq % 0-yq-He9q8qqQ8frFve9Fve9Ff0dmeaabaqaciaacaGaaeqabaWaae % aaeaaakeaatuuDJXwAK1uy0HMmaeXbfv3ySLgzG0uy0HgiuD3BaGqb % biab-vi8gnaaBaaaleaacaaIYaWaaWbaaWqabeaaryqr1ngBPrgaiy % GacqGF3bWDaaaaleqaaaaa!4C2E! $$ \mathbb{F}_{2ŵ } $$ -- Using Quasi-Monte Carlo Scenarios in Risk Management -- Adaptive Quasi-Monte Carlo Integration Based on MISER and VEGAS -- When Does Monte Carlo Depend Polynomially on the Number of Variables? -- A New Adaptive Method for Geometric Convergence -- Polynomial Arithmetic Analogue of Hickernell Sequences