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Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria /
"This is the proceedings of the "8th IMACS Seminar on Monte Carlo Methods" held from August 29 to September 2, 2011 in Borovets, Bulgaria, and organized by the Institute of Information and Communication Technologies of the Bulgarian Academy of Sciences in cooperation with the Internat...
| Clasificación: | Libro Electrónico |
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| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico Congresos, conferencias eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin :
De Gruyter,
2013.
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| Colección: | Proceedings in mathematics.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Preface; 1 Improvement of Multi-population Genetic Algorithms Convergence Time; 1.1 Introduction; 1.2 Short Overview of MpGA Modifications; 1.3 Parameter Identification of S. cerevisiae Fed-Batch Cultivation Using Different Kinds of MpGA; 1.4 Analysis and Conclusions; 2 Parallelization and Optimization of 4D Binary Mixture Monte Carlo Simulations Using Open MPI and CUDA; 2.1 Introduction; 2.2 The Metropolis Monte Carlo Method; 2.3 Decomposition into Subdomains and the Virtual Topology Using OpenMPI; 2.4 Management of Hypersphere Coordinate Migration Between Domains.
- 2.4.1 Communication between the CPU and the GPU2.5 Pseudorandom Number Generation; 2.6 Results of Running the Modified Code; 2.7 Conclusions; 3 Efficient Implementation of the Heston Model Using GPGPU; 3.1 Introduction; 3.2 Our GPGPU-Based Algorithm for Option Pricing; 3.3 Numerical Results; 3.4 Conclusions and Future Work; 4 On a Game-Method for Modeling with Intuitionistic Fuzzy Estimations. Part 2; 4.1 Introduction; 4.2 Short Remarks on the Game-Method for Modeling from Crisp Point of View; 4.3 On the Game-Method for Modeling with Intuitionistic Fuzzy Estimations; 4.4 Main Results.
- 4.5 Conclusion5 Generalized Nets, ACO Algorithms, and Genetic Algorithms; 5.1 Introduction; 5.2 ACO and GA; 5.3 GN for Hybrid ACO-GA Algorithm; 5.4 Conclusion; 6 Bias Evaluation and Reduction for Sample-Path Optimization; 6.1 Introduction; 6.2 Problem Formulation; 6.3 Taylor-Based Bias Correction; 6.4 Impact on the Optimization Bias; 6.5 Numerical Experiments; 6.6 Conclusions; 7 Monte Carlo Simulation of Electron Transport in Quantum Cascade Lasers; 7.1 Introduction; 7.2 QCL Transport Model; 7.2.1 Pauli Master Equation; 7.2.2 Calculation of Basis States; 7.2.3 Monte Carlo Solver.
- 7.3 Results and Discussion7.4 Conclusion; 8 Markov Chain Monte Carlo Particle Algorithms for Discrete-Time Nonlinear Filtering; 8.1 Introduction; 8.2 General Particle Filtering Framework; 8.3 High Dimensional Particle Schemes; 8.3.1 Sequential MCMC Filtering; 8.3.2 Efficient Sampling in High Dimensions; 8.3.3 Setting Proposal and Steering Distributions; 8.4 Illustrative Examples; 8.5 Conclusions; 9 Game-Method for Modeling and WRF-Fire Model Working Together; 9.1 Introduction; 9.2 Description of the Game-Method for Modeling.
- 9.3 General Description of the Coupled Atmosphere Fire Modeling and WRF-Fire9.4 Wind Simulation Approach; 9.5 Conclusion; 10 Wireless Sensor Network Layout; 10.1 Introduction; 10.2 Wireless Sensor Network Layout Problem; 10.3 ACO for WSN Layout Problem; 10.4 Experimental Results; 10.5 Conclusion; 11 A Two-Dimensional Lorentzian Distribution for an Atomic Force Microscopy Simulator; 11.1 Introduction; 11.2 Modeling Oxidation Kinetics; 11.3 Development of the Lorentzian Model; 11.3.1 Algorithm for the Gaussian Model; 11.3.2 Development of the Lorentzian Model; 11.4 Conclusion.