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Biblioteca

MARC Lista (tabellare)
Advanced Markov chain Monte Carlo methods : learning from past samples / / Faming Liang, Chuanhai Liu, Raymond J. Carroll
Advanced Markov chain Monte Carlo methods : learning from past samples / / Faming Liang, Chuanhai Liu, Raymond J. Carroll
Autore Liang F (Faming), <1970->
Pubbl/distr/stampa Hoboken, NJ, : Wiley, 2010
Descrizione fisica 1 online resource (379 p.)
Disciplina 518/.282
Altri autori (Persone) LiuChuanhai <1959->
CarrollRaymond J
Collana Wiley Series in Computational Statistics
Soggetto topico Monte Carlo method
Markov processes
ISBN 9786612661563
9781119956808
1119956803
9781282661561
1282661566
9780470669723
0470669721
9780470669730
047066973X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Advanced Markov Chain Monte Carlo Methods; Contents; Preface; Acknowledgments; Publisher's Acknowledgments; 1 Bayesian Inference and Markov Chain Monte Carlo; 2 The Gibbs Sampler; 3 The Metropolis-Hastings Algorithm; 4 Auxiliary Variable MCMC Methods; 5 Population-Based MCMC Methods; 6 Dynamic Weighting; 7 Stochastic Approximation Monte Carlo; 8 Markov Chain Monte Carlo with Adaptive Proposals; References; Index
Record Nr. UNINA-9910140556803321
Liang F (Faming), <1970->  
Hoboken, NJ, : Wiley, 2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Exploring Monte Carlo methods [[electronic resource] /] / William L. Dunn, J. Kenneth Shultis
Exploring Monte Carlo methods [[electronic resource] /] / William L. Dunn, J. Kenneth Shultis
Autore Dunn William L (William Lee), <1944->
Pubbl/distr/stampa Amsterdam ; ; Boston, : Elsevier, c2012
Descrizione fisica 1 online resource (401 p.)
Disciplina 510
518/.282
Altri autori (Persone) ShultisJ. Kenneth
Soggetto topico Monte Carlo method
Soggetto genere / forma Electronic books.
ISBN 1-283-17383-2
9786613173836
0-08-093061-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Exploring Monte Carlo Methods; Copyright; Dedication; Table of Contents; Preface; Chapter 1. Introduction; 1.1 What Is Monte Carlo?; 1.2 A Brief History of Monte Carlo; 1.3 Monte Carlo as Quadrature; 1.4 Monte Carlo as Simulation; 1.5 Preview of Things to Come; 1.6 Summary; Bibliography; Problems; Chapter 2. The Basis of Monte Carlo; 2.1 Single Continuous Random Variables; 2.2 Discrete Random Variables; 2.3 Multiple Random Variables; 2.4 The Law of Large Numbers; 2.5 The Central Limit Theorem; 2.6 Monte Carlo Quadrature; 2.7 Monte Carlo Simulation; 2.8 Summary; Bibliography
ProblemsChapter 3. Pseudorandom Number Generators; 3.1 Linear Congruential Generators; 3.2 Structure of the Generated Random Numbers; 3.3 Characteristics of Good Random Number Generators; 3.4 Tests for Congruential Generators; 3.5 Practical Multiplicative Congruential Generators; 3.6 Shuffling a Generator's Output; 3.7 Skipping Ahead; 3.8 Combining Generators; 3.9 Other Random Number Generators; 3.10 Summary; Bibliography; Problems; Chapter 4. Sampling, Scoring, and Precision; 4.1 Sampling; 4.2 Scoring; 4.3 Accuracy and Precision; 4.4 Summary; Bibliography; Problems
Chapter 5. Variance Reduction Techniques5.1 Use of Transformations; 5.2 Importance Sampling; 5.3 Systematic Sampling; 5.4 Stratified Sampling; 5.5 Correlated Sampling; 5.6 Partition of the Integration Volume; 5.7 Reduction of Dimensionality; 5.8 Russian Roulette and Splitting; 5.9 Combinations of Different Variance Reduction Techniques; 5.10 Biased Estimators; 5.11 Improved Monte Carlo Integration Schemes; 5.12 Summary; Bibliography; Problems; Chapter 6. Markov Chain Monte Carlo; 6.1 Markov Chains to the Rescue; 6.2 Brief Review of Probability Concepts; 6.3 Bayes Theorem
6.4 Inference and Decision Applications6.5 Summary; Bibliography; Problems; Chapter 7. Inverse Monte Carlo; 7.1 Formulation of the Inverse Problem; 7.2 Inverse Monte Carlo by Iteration; 7.3 Symbolic Monte Carlo; 7.4 Inverse Monte Carlo by Simulation; 7.5 General Applications of IMC; 7.6 Summary; Bibliography; Problems; Chapter 8. Linear Operator Equations; 8.1 Linear Algebraic Equations; 8.2 Linear Integral Equations; 8.3 Linear Differential Equations; 8.4 Eigenvalue Problems; 8.5 Summary; Bibliography; Problems; Chapter 9. The Fundamentals of Neutral Particle Transport
9.1 Description of the Radiation Field9.2 Radiation Interactions with the Medium; 9.3 Transport Equation; 9.4 Adjoint Transport Equation; 9.5 Summary; Bibliography; Problems; Chapter 10. Monte Carlo Simulation of Neutral Particle Transport; 10.1 Basic Approach for Monte Carlo Transport Simulations; 10.2 Geometry; 10.3 Sources; 10.4 Path-Length Estimation; 10.5 Purely Absorbing Media; 10.6 Type of Collision; 10.7 Time Dependence; 10.8 Particle Weights; 10.9 Scoring and Tallies; 10.10 An Example of One-Speed Particle Transport; 10.11 Monte Carlo Based on the Integral Transport Equation
10.12 Variance Reduction and Nonanalog Methods
Record Nr. UNINA-9910480088603321
Dunn William L (William Lee), <1944->  
Amsterdam ; ; Boston, : Elsevier, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Exploring Monte Carlo methods [[electronic resource] /] / William L. Dunn, J. Kenneth Shultis
Exploring Monte Carlo methods [[electronic resource] /] / William L. Dunn, J. Kenneth Shultis
Autore Dunn William L (William Lee), <1944->
Pubbl/distr/stampa Amsterdam ; ; Boston, : Elsevier, c2012
Descrizione fisica 1 online resource (401 p.)
Disciplina 510
518/.282
Altri autori (Persone) ShultisJ. Kenneth
Soggetto topico Monte Carlo method
ISBN 1-283-17383-2
9786613173836
0-08-093061-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Exploring Monte Carlo Methods; Copyright; Dedication; Table of Contents; Preface; Chapter 1. Introduction; 1.1 What Is Monte Carlo?; 1.2 A Brief History of Monte Carlo; 1.3 Monte Carlo as Quadrature; 1.4 Monte Carlo as Simulation; 1.5 Preview of Things to Come; 1.6 Summary; Bibliography; Problems; Chapter 2. The Basis of Monte Carlo; 2.1 Single Continuous Random Variables; 2.2 Discrete Random Variables; 2.3 Multiple Random Variables; 2.4 The Law of Large Numbers; 2.5 The Central Limit Theorem; 2.6 Monte Carlo Quadrature; 2.7 Monte Carlo Simulation; 2.8 Summary; Bibliography
ProblemsChapter 3. Pseudorandom Number Generators; 3.1 Linear Congruential Generators; 3.2 Structure of the Generated Random Numbers; 3.3 Characteristics of Good Random Number Generators; 3.4 Tests for Congruential Generators; 3.5 Practical Multiplicative Congruential Generators; 3.6 Shuffling a Generator's Output; 3.7 Skipping Ahead; 3.8 Combining Generators; 3.9 Other Random Number Generators; 3.10 Summary; Bibliography; Problems; Chapter 4. Sampling, Scoring, and Precision; 4.1 Sampling; 4.2 Scoring; 4.3 Accuracy and Precision; 4.4 Summary; Bibliography; Problems
Chapter 5. Variance Reduction Techniques5.1 Use of Transformations; 5.2 Importance Sampling; 5.3 Systematic Sampling; 5.4 Stratified Sampling; 5.5 Correlated Sampling; 5.6 Partition of the Integration Volume; 5.7 Reduction of Dimensionality; 5.8 Russian Roulette and Splitting; 5.9 Combinations of Different Variance Reduction Techniques; 5.10 Biased Estimators; 5.11 Improved Monte Carlo Integration Schemes; 5.12 Summary; Bibliography; Problems; Chapter 6. Markov Chain Monte Carlo; 6.1 Markov Chains to the Rescue; 6.2 Brief Review of Probability Concepts; 6.3 Bayes Theorem
6.4 Inference and Decision Applications6.5 Summary; Bibliography; Problems; Chapter 7. Inverse Monte Carlo; 7.1 Formulation of the Inverse Problem; 7.2 Inverse Monte Carlo by Iteration; 7.3 Symbolic Monte Carlo; 7.4 Inverse Monte Carlo by Simulation; 7.5 General Applications of IMC; 7.6 Summary; Bibliography; Problems; Chapter 8. Linear Operator Equations; 8.1 Linear Algebraic Equations; 8.2 Linear Integral Equations; 8.3 Linear Differential Equations; 8.4 Eigenvalue Problems; 8.5 Summary; Bibliography; Problems; Chapter 9. The Fundamentals of Neutral Particle Transport
9.1 Description of the Radiation Field9.2 Radiation Interactions with the Medium; 9.3 Transport Equation; 9.4 Adjoint Transport Equation; 9.5 Summary; Bibliography; Problems; Chapter 10. Monte Carlo Simulation of Neutral Particle Transport; 10.1 Basic Approach for Monte Carlo Transport Simulations; 10.2 Geometry; 10.3 Sources; 10.4 Path-Length Estimation; 10.5 Purely Absorbing Media; 10.6 Type of Collision; 10.7 Time Dependence; 10.8 Particle Weights; 10.9 Scoring and Tallies; 10.10 An Example of One-Speed Particle Transport; 10.11 Monte Carlo Based on the Integral Transport Equation
10.12 Variance Reduction and Nonanalog Methods
Record Nr. UNINA-9910785584903321
Dunn William L (William Lee), <1944->  
Amsterdam ; ; Boston, : Elsevier, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Exploring Monte Carlo methods / / William L. Dunn, J. Kenneth Shultis
Exploring Monte Carlo methods / / William L. Dunn, J. Kenneth Shultis
Autore Dunn William L (William Lee), <1944->
Edizione [1st ed.]
Pubbl/distr/stampa Amsterdam ; ; Boston, : Elsevier, c2012
Descrizione fisica 1 online resource (401 p.)
Disciplina 518/.282
Altri autori (Persone) ShultisJ. Kenneth
Soggetto topico Monte Carlo method
ISBN 9786613173836
9781283173834
1283173832
9780080930619
0080930611
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Exploring Monte Carlo Methods; Copyright; Dedication; Table of Contents; Preface; Chapter 1. Introduction; 1.1 What Is Monte Carlo?; 1.2 A Brief History of Monte Carlo; 1.3 Monte Carlo as Quadrature; 1.4 Monte Carlo as Simulation; 1.5 Preview of Things to Come; 1.6 Summary; Bibliography; Problems; Chapter 2. The Basis of Monte Carlo; 2.1 Single Continuous Random Variables; 2.2 Discrete Random Variables; 2.3 Multiple Random Variables; 2.4 The Law of Large Numbers; 2.5 The Central Limit Theorem; 2.6 Monte Carlo Quadrature; 2.7 Monte Carlo Simulation; 2.8 Summary; Bibliography
ProblemsChapter 3. Pseudorandom Number Generators; 3.1 Linear Congruential Generators; 3.2 Structure of the Generated Random Numbers; 3.3 Characteristics of Good Random Number Generators; 3.4 Tests for Congruential Generators; 3.5 Practical Multiplicative Congruential Generators; 3.6 Shuffling a Generator's Output; 3.7 Skipping Ahead; 3.8 Combining Generators; 3.9 Other Random Number Generators; 3.10 Summary; Bibliography; Problems; Chapter 4. Sampling, Scoring, and Precision; 4.1 Sampling; 4.2 Scoring; 4.3 Accuracy and Precision; 4.4 Summary; Bibliography; Problems
Chapter 5. Variance Reduction Techniques5.1 Use of Transformations; 5.2 Importance Sampling; 5.3 Systematic Sampling; 5.4 Stratified Sampling; 5.5 Correlated Sampling; 5.6 Partition of the Integration Volume; 5.7 Reduction of Dimensionality; 5.8 Russian Roulette and Splitting; 5.9 Combinations of Different Variance Reduction Techniques; 5.10 Biased Estimators; 5.11 Improved Monte Carlo Integration Schemes; 5.12 Summary; Bibliography; Problems; Chapter 6. Markov Chain Monte Carlo; 6.1 Markov Chains to the Rescue; 6.2 Brief Review of Probability Concepts; 6.3 Bayes Theorem
6.4 Inference and Decision Applications6.5 Summary; Bibliography; Problems; Chapter 7. Inverse Monte Carlo; 7.1 Formulation of the Inverse Problem; 7.2 Inverse Monte Carlo by Iteration; 7.3 Symbolic Monte Carlo; 7.4 Inverse Monte Carlo by Simulation; 7.5 General Applications of IMC; 7.6 Summary; Bibliography; Problems; Chapter 8. Linear Operator Equations; 8.1 Linear Algebraic Equations; 8.2 Linear Integral Equations; 8.3 Linear Differential Equations; 8.4 Eigenvalue Problems; 8.5 Summary; Bibliography; Problems; Chapter 9. The Fundamentals of Neutral Particle Transport
9.1 Description of the Radiation Field9.2 Radiation Interactions with the Medium; 9.3 Transport Equation; 9.4 Adjoint Transport Equation; 9.5 Summary; Bibliography; Problems; Chapter 10. Monte Carlo Simulation of Neutral Particle Transport; 10.1 Basic Approach for Monte Carlo Transport Simulations; 10.2 Geometry; 10.3 Sources; 10.4 Path-Length Estimation; 10.5 Purely Absorbing Media; 10.6 Type of Collision; 10.7 Time Dependence; 10.8 Particle Weights; 10.9 Scoring and Tallies; 10.10 An Example of One-Speed Particle Transport; 10.11 Monte Carlo Based on the Integral Transport Equation
10.12 Variance Reduction and Nonanalog Methods
Record Nr. UNINA-9910971661203321
Dunn William L (William Lee), <1944->  
Amsterdam ; ; Boston, : Elsevier, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fast sequential Monte Carlo methods for counting and optimization / / Reuven Rubinstein, Ad Ridder, Radislav Vaisman
Fast sequential Monte Carlo methods for counting and optimization / / Reuven Rubinstein, Ad Ridder, Radislav Vaisman
Autore Rubinstein Reuven Y
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014]
Descrizione fisica 1 online resource (208 p.)
Disciplina 518/.282
Altri autori (Persone) RidderAd <1955->
VaismanRadislav
Collana Wiley series in probability and statistics
Soggetto topico Mathematical optimization
Monte Carlo method
ISBN 1-118-61235-3
1-118-61232-9
1-118-61231-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Contents; Preface; Chapter 1 Introduction to Monte Carlo Methods; Chapter 2 Cross-Entropy Method; 2.1. Introduction; 2.2. Estimation of Rare-Event Probabilities; 2.3. Cross-Entrophy Method for Optimization; 2.3.1. The Multidimensional 0/1 Knapsack Problem; 2.3.2. Mastermind Game; 2.3.3. Markov Decision Process and Reinforcement Learning; 2.4. Continuous Optimization; 2.5. Noisy Optimization; 2.5.1. Stopping Criterion; Chapter 3 Minimum Cross-Entropy Method; 3.1. Introduction; 3.2. Classic MinxEnt Method; 3.3. Rare Events and MinxEnt; 3.4. Indicator MinxEnt Method
3.4.1. Connection between CE and IME3.5. IME Method for Combinatorial Optimization; 3.5.1. Unconstrained Combinatorial Optimization; 3.5.2. Constrained Combinatorial Optimization: The Penalty Function Approach; Chapter 4 Splitting Method for Counting and Optimization; 4.1. Background; 4.2. Quick Glance at the Splitting Method; 4.3. Splitting Algorithm with Fixed Levels; 4.4. Adaptive Splitting Algorithm; 4.5. Sampling Uniformly on Discrete Regions; 4.6. Splitting Algorithm for Combinatorial Optimization; 4.7. Enhanced Splitting Method for Counting; 4.7.1. Counting with the Direct Estimator
4.7.2. Counting with the Capture-Recapture Method4.8. Application of Splitting to Reliability Models; 4.8.1. Introduction; 4.8.2. Static Graph Reliability Problem; 4.8.3. BMC Algorithm for Computing S(Y); 4.8.4. Gibbs Sampler; 4.9. Numerical Results with the Splitting Algorithms; 4.9.1. Counting; 4.9.2. Combinatorial Optimization; 4.9.3. Reliability Models; 4.10. Appendix: Gibbs Sampler; Chapter 5 Stochastic Enumeration Method; 5.1. Introduction; 5.2. OSLA Method and Its Extensions; 5.2.1. Extension of OSLA: nSLA Method; 5.2.2. Extension of OSLA for SAW: Multiple Trajectories; 5.3. SE Method
5.3.1. SE Algorithm5.4. Applications of SE; 5.4.1. Counting the Number of Trajectories in a Network; 5.4.2. SE for Probabilities Estimation; 5.4.3. Counting the Number of Perfect Matchings in a Graph; 5.4.4. Counting SAT; 5.5. Numerical Results; 5.5.1. Counting SAW; 5.5.2. Counting the Number of Trajectories in a Network; 5.5.3. Counting the Number of Perfect Matchings in a Graph; 5.5.4. Counting SAT; 5.5.5. Comparison of SE with Splitting and SampleSearch; Appendix A Additional Topics; A.1. Combinatorial Problems; A.1.1. Counting; A.1.2. Combinatorial Optimization; A.2. Information
A.2.1. Shannon EntropyA.2.2. Kullback-Leibler Cross-Entropy; A.3. Efficiency of Estimators; A.3.1. Complexity; A.3.2. Complexity of Randomized Algorithms; Bibliography; Abbreviations and Acronyms; List of Symbols; Index; Series Page
Record Nr. UNINA-9910139030003321
Rubinstein Reuven Y  
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fast sequential Monte Carlo methods for counting and optimization / / Reuven Rubinstein, Ad Ridder, Radislav Vaisman
Fast sequential Monte Carlo methods for counting and optimization / / Reuven Rubinstein, Ad Ridder, Radislav Vaisman
Autore Rubinstein Reuven Y
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014]
Descrizione fisica 1 online resource (208 p.)
Disciplina 518/.282
Altri autori (Persone) RidderAd <1955->
VaismanRadislav
Collana Wiley series in probability and statistics
Soggetto topico Mathematical optimization
Monte Carlo method
ISBN 9781118612354
1118612353
9781118612323
1118612329
9781118612316
1118612310
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Contents; Preface; Chapter 1 Introduction to Monte Carlo Methods; Chapter 2 Cross-Entropy Method; 2.1. Introduction; 2.2. Estimation of Rare-Event Probabilities; 2.3. Cross-Entrophy Method for Optimization; 2.3.1. The Multidimensional 0/1 Knapsack Problem; 2.3.2. Mastermind Game; 2.3.3. Markov Decision Process and Reinforcement Learning; 2.4. Continuous Optimization; 2.5. Noisy Optimization; 2.5.1. Stopping Criterion; Chapter 3 Minimum Cross-Entropy Method; 3.1. Introduction; 3.2. Classic MinxEnt Method; 3.3. Rare Events and MinxEnt; 3.4. Indicator MinxEnt Method
3.4.1. Connection between CE and IME3.5. IME Method for Combinatorial Optimization; 3.5.1. Unconstrained Combinatorial Optimization; 3.5.2. Constrained Combinatorial Optimization: The Penalty Function Approach; Chapter 4 Splitting Method for Counting and Optimization; 4.1. Background; 4.2. Quick Glance at the Splitting Method; 4.3. Splitting Algorithm with Fixed Levels; 4.4. Adaptive Splitting Algorithm; 4.5. Sampling Uniformly on Discrete Regions; 4.6. Splitting Algorithm for Combinatorial Optimization; 4.7. Enhanced Splitting Method for Counting; 4.7.1. Counting with the Direct Estimator
4.7.2. Counting with the Capture-Recapture Method4.8. Application of Splitting to Reliability Models; 4.8.1. Introduction; 4.8.2. Static Graph Reliability Problem; 4.8.3. BMC Algorithm for Computing S(Y); 4.8.4. Gibbs Sampler; 4.9. Numerical Results with the Splitting Algorithms; 4.9.1. Counting; 4.9.2. Combinatorial Optimization; 4.9.3. Reliability Models; 4.10. Appendix: Gibbs Sampler; Chapter 5 Stochastic Enumeration Method; 5.1. Introduction; 5.2. OSLA Method and Its Extensions; 5.2.1. Extension of OSLA: nSLA Method; 5.2.2. Extension of OSLA for SAW: Multiple Trajectories; 5.3. SE Method
5.3.1. SE Algorithm5.4. Applications of SE; 5.4.1. Counting the Number of Trajectories in a Network; 5.4.2. SE for Probabilities Estimation; 5.4.3. Counting the Number of Perfect Matchings in a Graph; 5.4.4. Counting SAT; 5.5. Numerical Results; 5.5.1. Counting SAW; 5.5.2. Counting the Number of Trajectories in a Network; 5.5.3. Counting the Number of Perfect Matchings in a Graph; 5.5.4. Counting SAT; 5.5.5. Comparison of SE with Splitting and SampleSearch; Appendix A Additional Topics; A.1. Combinatorial Problems; A.1.1. Counting; A.1.2. Combinatorial Optimization; A.2. Information
A.2.1. Shannon EntropyA.2.2. Kullback-Leibler Cross-Entropy; A.3. Efficiency of Estimators; A.3.1. Complexity; A.3.2. Complexity of Randomized Algorithms; Bibliography; Abbreviations and Acronyms; List of Symbols; Index; Series Page
Record Nr. UNINA-9910807943703321
Rubinstein Reuven Y  
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of Monte Carlo methods / / Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev
Handbook of Monte Carlo methods / / Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev
Autore Kroese Dirk P
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2011
Descrizione fisica 1 online resource (752 p.)
Disciplina 518/.282
Altri autori (Persone) TaimreThomas <1983->
BotevZdravko I. <1982->
Collana Wiley series in probability and statistics
Soggetto topico Monte Carlo method
ISBN 9786613072429
9781283072427
1283072424
9781118014950
1118014952
9781118014967
1118014960
9781118014943
1118014944
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910141148203321
Kroese Dirk P  
Hoboken, N.J., : Wiley, 2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Monte Carlo method, random number, and pseudorandom number / / Hiroshi Sugita
Monte Carlo method, random number, and pseudorandom number / / Hiroshi Sugita
Autore Sugita Hiroshi <1958->
Pubbl/distr/stampa Mathematical Society of Japan
Descrizione fisica 133 pages : illustrations ; ; 25 cm
Disciplina 518/.282
Collana MSJ memoirs
Soggetto topico Monte Carlo method
Numbers, Random
Random number generators
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Monte Carlo method, random number, and pseudorandom number. Vol 25
Record Nr. UNINA-9910390550803321
Sugita Hiroshi <1958->  
Mathematical Society of Japan
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Monte Carlo methods and applications [[electronic resource] ] : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / / edited by Karl K. Sabelfeld, Ivan Dimov
Monte Carlo methods and applications [[electronic resource] ] : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / / edited by Karl K. Sabelfeld, Ivan Dimov
Pubbl/distr/stampa Berlin, : De Gruyter, 2013
Descrizione fisica 1 online resource (248 p.)
Disciplina 518/.282
Altri autori (Persone) SabelfeldKarl K
DimovIvan
Collana De Gruyter proceedings in mathematics
Soggetto topico Monte Carlo method
Mathematics
Soggetto genere / forma Electronic books.
ISBN 3-11-029358-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Frontmatter -- Preface -- Contents -- Chapter 1. Improvement of Multi-population Genetic Algorithms Convergence Time / Angelova, Maria / Pencheva, Tania -- Chapter 2 Parallelization and Optimization of 4D Binary Mixture Monte Carlo Simulations Using Open MPI and CUDA / Artemchuk, Sergey / Whitlock, Paula A. -- Chapter 3 Efficient Implementation of the Heston Model Using GPGPU / Atanassov, Emanouil / Dimitrov, Dimitar / Ivanovska, Sofiya -- Chapter 4. On a Game-Method for Modeling with Intuitionistic Fuzzy Estimations. Part 2 / Atanassova, Lilija / Atanassov, Krassimir -- Chapter 5. Generalized Nets, ACO Algorithms, and Genetic Algorithms / Atanassova, Vassia / Fidanova, Stefka / Popchev, Ivan / Chountas, Panagiotis -- Chapter 6. Bias Evaluation and Reduction for Sample-Path Optimization / / Bastin, Fabian -- Chapter 7. Monte Carlo Simulation of Electron Transport in Quantum Cascade Lasers / Baumgartner, Oskar / Stanojević, Zlatan / Kosina, Hans -- Chapter 8. Markov Chain Monte Carlo Particle Algorithms for Discrete-Time Nonlinear Filtering / Carmi, Avishy / Mihaylova, Lyudmila -- Chapter 9. Game-Method for Modeling and WRF-Fire Model Working Together / Dobrinkova, Nina / Fidanova, Stefka / Dimov, Ivan / Atanassov, Krassimir / Mandel, Jan -- Chapter 10. Wireless Sensor Network Layout / Fidanova, Stefka / Marinov, Pencho / Alba, Enrique -- Chapter 11. A Two-Dimensional Lorentzian Distribution for an Atomic Force Microscopy Simulator / Filipovic, Lado / Selberherr, Siegfried -- Chapter 12. Stratified Monte Carlo Integration / Haddad, Rami El / Fakhreddine, Rana / Lécot, Christian -- Chapter 13. Monte Carlo Simulation of Asymmetric Flow Field Flow Fractionation / Iliev, Oleg / Nagapetyan, Tigran / Ritter, Klaus -- Chapter 14. Convexization in Markov Chain Monte Carlo / Kanevsky, Dimitri / Carmi, Avishy -- Chapter 15. Value Simulation of the Interacting Pair Number for Solution of the Monodisperse Coagulation Equation / Korotchenko, Mariya / Burmistrov, Aleksandr -- Chapter 16. Parallelization of Algorithms for Solving a Three-Dimensional Sudoku Puzzle / Mayorov, Mikhail / Whitlock, Paula A. -- Chapter 17. The Efficiency Study of Splitting and Branching in the Monte Carlo Method / Medvedev, Ilya N. -- Chapter 18. On the Asymptotics of a Lower Bound for the Diaphony of Generalized van der Corput Sequences / Pausinger, Florian / Schmid, Wolfgang Ch. -- Chapter 19. Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterized Likelihood Function / Petrov, Nikolay / Mihaylova, Lyudmila / Gning, Amadou / Angelova, Donka -- Chapter 20. The Template Design Problem: A Perspective with Metaheuristics / Rueda, David Rodríguez / Cotta, Carlos / Fernández-Leiva, Antonio J. -- Chapter 21. A Comparison of Simulated Annealing and Genetic Algorithm Approaches for Cultivation Model Identification / Roeva, Olympia -- Chapter 22. Monte Carlo Investigations of Electron Decoherence due to Phonons / Schwaha, Philipp / Nedjalkov, Mihail / Selberherr, Siegfried / Dimov, Ivan -- Chapter 23. Geometric Allocation Approach for the Transition Kernel of a Markov Chain / Suwa, Hidemaro / Todo, Synge -- Chapter 24. Exact Sampling for the Ising Model at All Temperatures / Ullrich, Mario
Record Nr. UNINA-9910452897703321
Berlin, : De Gruyter, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Monte Carlo methods and applications [[electronic resource] ] : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / / edited by Karl K. Sabelfeld, Ivan Dimov
Monte Carlo methods and applications [[electronic resource] ] : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / / edited by Karl K. Sabelfeld, Ivan Dimov
Pubbl/distr/stampa Berlin, : De Gruyter, 2013
Descrizione fisica 1 online resource (248 p.)
Disciplina 518/.282
Altri autori (Persone) SabelfeldKarl K
DimovIvan
Collana De Gruyter proceedings in mathematics
Soggetto topico Monte Carlo method
Mathematics
Soggetto non controllato Monte Carlo Method, Stochastic Model, Financial Mathematics
ISBN 3-11-029358-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Frontmatter -- Preface -- Contents -- Chapter 1. Improvement of Multi-population Genetic Algorithms Convergence Time / Angelova, Maria / Pencheva, Tania -- Chapter 2 Parallelization and Optimization of 4D Binary Mixture Monte Carlo Simulations Using Open MPI and CUDA / Artemchuk, Sergey / Whitlock, Paula A. -- Chapter 3 Efficient Implementation of the Heston Model Using GPGPU / Atanassov, Emanouil / Dimitrov, Dimitar / Ivanovska, Sofiya -- Chapter 4. On a Game-Method for Modeling with Intuitionistic Fuzzy Estimations. Part 2 / Atanassova, Lilija / Atanassov, Krassimir -- Chapter 5. Generalized Nets, ACO Algorithms, and Genetic Algorithms / Atanassova, Vassia / Fidanova, Stefka / Popchev, Ivan / Chountas, Panagiotis -- Chapter 6. Bias Evaluation and Reduction for Sample-Path Optimization / / Bastin, Fabian -- Chapter 7. Monte Carlo Simulation of Electron Transport in Quantum Cascade Lasers / Baumgartner, Oskar / Stanojević, Zlatan / Kosina, Hans -- Chapter 8. Markov Chain Monte Carlo Particle Algorithms for Discrete-Time Nonlinear Filtering / Carmi, Avishy / Mihaylova, Lyudmila -- Chapter 9. Game-Method for Modeling and WRF-Fire Model Working Together / Dobrinkova, Nina / Fidanova, Stefka / Dimov, Ivan / Atanassov, Krassimir / Mandel, Jan -- Chapter 10. Wireless Sensor Network Layout / Fidanova, Stefka / Marinov, Pencho / Alba, Enrique -- Chapter 11. A Two-Dimensional Lorentzian Distribution for an Atomic Force Microscopy Simulator / Filipovic, Lado / Selberherr, Siegfried -- Chapter 12. Stratified Monte Carlo Integration / Haddad, Rami El / Fakhreddine, Rana / Lécot, Christian -- Chapter 13. Monte Carlo Simulation of Asymmetric Flow Field Flow Fractionation / Iliev, Oleg / Nagapetyan, Tigran / Ritter, Klaus -- Chapter 14. Convexization in Markov Chain Monte Carlo / Kanevsky, Dimitri / Carmi, Avishy -- Chapter 15. Value Simulation of the Interacting Pair Number for Solution of the Monodisperse Coagulation Equation / Korotchenko, Mariya / Burmistrov, Aleksandr -- Chapter 16. Parallelization of Algorithms for Solving a Three-Dimensional Sudoku Puzzle / Mayorov, Mikhail / Whitlock, Paula A. -- Chapter 17. The Efficiency Study of Splitting and Branching in the Monte Carlo Method / Medvedev, Ilya N. -- Chapter 18. On the Asymptotics of a Lower Bound for the Diaphony of Generalized van der Corput Sequences / Pausinger, Florian / Schmid, Wolfgang Ch. -- Chapter 19. Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterized Likelihood Function / Petrov, Nikolay / Mihaylova, Lyudmila / Gning, Amadou / Angelova, Donka -- Chapter 20. The Template Design Problem: A Perspective with Metaheuristics / Rueda, David Rodríguez / Cotta, Carlos / Fernández-Leiva, Antonio J. -- Chapter 21. A Comparison of Simulated Annealing and Genetic Algorithm Approaches for Cultivation Model Identification / Roeva, Olympia -- Chapter 22. Monte Carlo Investigations of Electron Decoherence due to Phonons / Schwaha, Philipp / Nedjalkov, Mihail / Selberherr, Siegfried / Dimov, Ivan -- Chapter 23. Geometric Allocation Approach for the Transition Kernel of a Markov Chain / Suwa, Hidemaro / Todo, Synge -- Chapter 24. Exact Sampling for the Ising Model at All Temperatures / Ullrich, Mario
Record Nr. UNINA-9910779727503321
Berlin, : De Gruyter, 2013
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