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...
Gespeichert in:
| Körperschaft: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch Tagungsbericht E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin :
De Gruyter,
2013.
|
| Schriftenreihe: | Proceedings in mathematics.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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 International Association for Mathematics and Computers in Simulation (IMACS). Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures."--Publisher's website. |
| Beschreibung: | 1 online resource (xiii, 233 pages) : illustrations |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9783110293586 3110293587 3110293471 9783110293470 |
Internformat
MARC
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| 111 | 2 | |a IMACS Seminar on Monte Carlo Methods |n (8th : |d 2011 : |c Borovet︠s︡, Bulgaria) | |
| 245 | 1 | 0 | |a Monte Carlo methods and applications : |b proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / |c edited by Karl K. Sabelfeld, Ivan Dimov. |
| 260 | |a Berlin : |b De Gruyter, |c 2013. | ||
| 300 | |a 1 online resource (xiii, 233 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a De Gruyter proceedings in mathematics | |
| 504 | |a Includes bibliographical references. | ||
| 520 | |a "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 International Association for Mathematics and Computers in Simulation (IMACS). Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures."--Publisher's website. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 546 | |a In English. | ||
| 650 | 0 | |a Monte Carlo method |v Congresses. | |
| 650 | 0 | |a Mathematics |v Congresses. | |
| 650 | 6 | |a Méthode de Monte-Carlo |v Congrès. | |
| 650 | 6 | |a Mathématiques |v Congrès. | |
| 650 | 7 | |a MATHEMATICS |x Numerical Analysis. |2 bisacsh | |
| 650 | 7 | |a Mathematics |2 fast | |
| 650 | 7 | |a Monte Carlo method |2 fast | |
| 655 | 7 | |a Conference papers and proceedings |2 fast | |
| 700 | 1 | |a Sabelʹfelʹd, K. K. |q (Karl Karlovich), |e editor. | |
| 700 | 1 | |a Dimov, Ivan, |d 1963- |e editor. | |
| 776 | 0 | 8 | |i Print version: |a IMACS Seminar on Monte Carlo Methods (8th : 2011 : Borovet︠s, Bulgaria). |t Monte Carlo methods and applications. |d [Berlin] : De Gruyter, 2013 |z 9783110293470 |w (OCoLC)816818796 |
| 830 | 0 | |a Proceedings in mathematics. | |
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| author2 | Sabelʹfelʹd, K. K. (Karl Karlovich) Dimov, Ivan, 1963- |
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| author_corporate | IMACS Seminar on Monte Carlo Methods Borovet︠s︡, Bulgaria |
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| author_sort | IMACS Seminar on Monte Carlo Methods Borovet︠s︡, Bulgaria |
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| contents | 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. |
| ctrlnum | (OCoLC)828738579 |
| dewey-full | 518/.282 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 518 - Numerical analysis |
| dewey-raw | 518/.282 |
| dewey-search | 518/.282 |
| dewey-sort | 3518 3282 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic Conference Proceeding eBook |
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Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures."--Publisher's website.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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. 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| genre | Conference papers and proceedings fast |
| genre_facet | Conference papers and proceedings |
| id | ZDB-4-EBA-ocn828738579 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:25:12Z |
| institution | BVB |
| isbn | 9783110293586 3110293587 3110293471 9783110293470 |
| language | English |
| lccn | 2012538466 |
| oclc_num | 828738579 |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xiii, 233 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | De Gruyter, |
| record_format | marc |
| series | Proceedings in mathematics. |
| series2 | De Gruyter proceedings in mathematics |
| spelling | IMACS Seminar on Monte Carlo Methods (8th : 2011 : Borovet︠s︡, Bulgaria) Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / edited by Karl K. Sabelfeld, Ivan Dimov. Berlin : De Gruyter, 2013. 1 online resource (xiii, 233 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier De Gruyter proceedings in mathematics Includes bibliographical references. "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 International Association for Mathematics and Computers in Simulation (IMACS). Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures."--Publisher's website. Print version record. 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. In English. Monte Carlo method Congresses. Mathematics Congresses. Méthode de Monte-Carlo Congrès. Mathématiques Congrès. MATHEMATICS Numerical Analysis. bisacsh Mathematics fast Monte Carlo method fast Conference papers and proceedings fast Sabelʹfelʹd, K. K. (Karl Karlovich), editor. Dimov, Ivan, 1963- editor. Print version: IMACS Seminar on Monte Carlo Methods (8th : 2011 : Borovet︠s, Bulgaria). Monte Carlo methods and applications. [Berlin] : De Gruyter, 2013 9783110293470 (OCoLC)816818796 Proceedings in mathematics. FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=530607 Volltext |
| spellingShingle | Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / Proceedings in mathematics. 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. Monte Carlo method Congresses. Mathematics Congresses. Méthode de Monte-Carlo Congrès. Mathématiques Congrès. MATHEMATICS Numerical Analysis. bisacsh Mathematics fast Monte Carlo method fast |
| title | Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / |
| title_auth | Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / |
| title_exact_search | Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / |
| title_full | Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / edited by Karl K. Sabelfeld, Ivan Dimov. |
| title_fullStr | Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / edited by Karl K. Sabelfeld, Ivan Dimov. |
| title_full_unstemmed | Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / edited by Karl K. Sabelfeld, Ivan Dimov. |
| title_short | Monte Carlo methods and applications : |
| title_sort | monte carlo methods and applications proceedings of the 8th imacs seminar on monte carlo methods august 29 september 2 2011 borovets bulgaria |
| title_sub | proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria / |
| topic | Monte Carlo method Congresses. Mathematics Congresses. Méthode de Monte-Carlo Congrès. Mathématiques Congrès. MATHEMATICS Numerical Analysis. bisacsh Mathematics fast Monte Carlo method fast |
| topic_facet | Monte Carlo method Congresses. Mathematics Congresses. Méthode de Monte-Carlo Congrès. Mathématiques Congrès. MATHEMATICS Numerical Analysis. Mathematics Monte Carlo method Conference papers and proceedings |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=530607 |
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