Tag: Carlo

Free Download Ivan Tomov Dimov, "Monte Carlo Methods For Applied Scientists"
English | 2005 | pages: 308 | ISBN: 9810223293 | PDF | 1,5 mb
The Monte Carlo method is inherently parallel and the extensive and rapid development in parallel computers, computational clusters and grids has resulted in renewed and increasing interest in this method. At the same time there has been an expansion in the application areas and the method is now widely used in many important areas of science including nuclear and semiconductor physics, statistical mechanics and heat and mass transfer.This book attempts to bridge the gap between theory and practice concentrating on modern algorithmic implementation on parallel architecture machines. Although a suitable text for final year postgraduate mathematicians and computational scientists it is principally aimed at the applied scientists: only a small amount of mathematical knowledge is assumed and theorem proving is kept to a minimum, with the main focus being on parallel algorithms development often to applied industrial problems.A selection of algorithms developed both for serial and parallel machines are provided.

Free Download The Monte Carlo Method in Condensed Matter Physics By Kurt Binder (auth.), Professor Dr. Kurt Binder (eds.)
1995 | 420 Pages | ISBN: 3540601740 | PDF | 7 MB
Alongside experimental and theoretical work, computer simulation now forms one of the major tools of research in physics. The Monte Carlo method is the most important simulation method in the area of condensed matter physics. This book, written by foremost experts in the field, describes the state of the art of simulation methods in solid state physics. It also reviews selected applications in areas of particular current interest like simulations of growth processes far from equilibrium, interfacial phenomena, quantum and classical fluids, polymers, quantum problems on lattices, and random systems. A new chapter on recent developments in the Monte Carlo simulation of condensed matter has been attached.

Free Download Harald Niederreiter, Denis Talay, "Monte Carlo and Quasi-Monte Carlo Methods 2004"
English | 2005 | pages: 506 | ISBN: 3540255419 | PDF | 6,0 mb
This volume represents the refereed proceedings of the Sixth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scienti?c Computing which was held in conjunction with the Second International C- ference on Monte Carlo and Probabilistic Methods for Partial Di?erential Equations at Juan-les-Pins, France, from 7-10 June 2004. The programme of this conference was arranged by a committee consisting of Henri Faure (U- versit´ edeMarseille), PaulGlasserman(ColumbiaUniversity), StefanHeinrich (Universit] at Kaiserslautern), Fred J. Hickernell (Hong Kong Baptist Univ- sity), Damien Lamberton (Universit´ e de Marne la Vall´ ee), Bernard Lapeyre (ENPC-CERMICS), Pierre L’Ecuyer (Universit´edeMontr´ eal), Pierre-Louis Lions (Coll` ege de France), Harald Niederreiter (National University of S- gapore, co-chair), Erich Novak (Universit] at Jena), Art B. Owen (Stanford University), Gilles Pag` es (Universit´ e Paris 6), Philip Protter (Cornell U- versity), Ian H. Sloan (University of New South Wales), Denis Talay (INRIA Sophia Antipolis, co-chair), Simon Tavar´ e (University of Southern California) and Henryk Wo´ zniakowski (Columbia University and University of Warsaw). The organization of the conference was arranged by a committee consisting of Mireille Bossy and Etienne Tanr´ e (INRIA Sophia Antipolis), and Madalina Deaconu(INRIALorraine). LocalarrangementswereinthehandsofMonique Simonetti and Marie-Line Ramfos (INRIA Sophia Antipolis).

Free Download Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing: Proceedings of a conference at the University of Nevada, Las Vegas, Nevada, USA, June 23-25, 1994 By Russel E. Caflisch, Bradley Moskowitz (auth.), Harald Niederreiter, Peter Jau-Shyong Shiue (eds.)
1995 | 372 Pages | ISBN: 0387945776 | PDF | 12 MB
Scientists and engineers are increasingly making use of simulation methods to solve problems which are insoluble by analytical techniques. Monte Carlo methods which make use of probabilistic simulations are frequently used in areas such as numerical integration, complex scheduling, queueing networks, and large-dimensional simulations. This collection of papers arises from a conference held at the University of Nevada, Las Vegas, in 1994. The conference brought together researchers across a range of disciplines whose interests include the theory and application of these methods. This volume provides a timely survey of this field and the new directions in which the field is moving.

Free Download Introduction to @RISK. Monte Carlo Simulation addin on Excel
Last updated 2/2020
Created by Fernando Hernandez
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 15 Lectures ( 1h 21m ) | Size: 884 MB

Free Download Advances in Chemical Physics: Monte Carlo Methods in Chemical Physics, Volume 105 By
1998 | 561 Pages | ISBN: 0471196304 | PDF | 18 MB
In Monte Carlo Methods in Chemical Physics: An Introduction to the Monte Carlo Method for Particle Simulations J. Ilja Siepmann Random Number Generators for Parallel Applications Ashok Srinivasan, David M. Ceperley and Michael Mascagni Between Classical and Quantum Monte Carlo Methods: "Variational" QMC Dario Bressanini and Peter J. Reynolds Monte Carlo Eigenvalue Methods in Quantum Mechanics and Statistical Mechanics M. P. Nightingale and C.J. Umrigar Adaptive Path-Integral Monte Carlo Methods for Accurate Computation of Molecular Thermodynamic Properties Robert Q. Topper Monte Carlo Sampling for Classical Trajectory Simulations Gilles H. Peslherbe Haobin Wang and William L. Hase Monte Carlo Approaches to the Protein Folding Problem Jeffrey Skolnick and Andrzej Kolinski Entropy Sampling Monte Carlo for Polypeptides and Proteins Harold A. Scheraga and Minh-Hong Hao Macrostate Dissection of Thermodynamic Monte Carlo Integrals Bruce W. Church, Alex Ulitsky, and David Shalloway Simulated Annealing-Optimal Histogram Methods David M. Ferguson and David G. Garrett Monte Carlo Methods for Polymeric Systems Juan J. de Pablo and Fernando A. Escobedo Thermodynamic-Scaling Methods in Monte Carlo and Their Application to Phase Equilibria John Valleau Semigrand Canonical Monte Carlo Simulation: Integration Along Coexistence Lines David A. Kofke Monte Carlo Methods for Simulating Phase Equilibria of Complex Fluids J. Ilja Siepmann Reactive Canonical Monte Carlo J. Karl Johnson New Monte Carlo Algorithms for Classical Spin Systems G. T. Barkema and M.E.J. NewmanContent:

Free Download Simulation and the Monte Carlo Method, Second Edition By Reuven Y. Rubinstein, Dirk P. Kroese(auth.)
2008 | 366 Pages | ISBN: 0470177942 | PDF | 8 MB
This accessible new edition explores the major topics in Monte Carlo simulationSimulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo Variance reduction techniques such as the transform likelihood ratio method and the screening method The score function method for sensitivity analysis The stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization The cross-entropy method to rare events estimation and combinatorial optimization Application of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy method An extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems, and sample MATLAB programs. Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method.Content: Chapter 1 Preliminaries (pages 1-47): Chapter 2 Random Number, Random Variable, and Stochastic Process Generation (pages 49-80): Chapter 3 Simulation of Discrete?Event Systems (pages 81-96): Chapter 4 Statistical Analysis of Discrete?Event Systems (pages 97-118): Chapter 5 Controlling the Variance (pages 119-166): Chapter 6 Markov Chain Monte Carlo (pages 167-200): Chapter 7 Sensitivity Analysis and Monte Carlo Optimization (pages 201-234): Chapter 8 The Cross?Entropy Method (pages 235-277): Chapter 9 Counting via Monte Carlo (pages 279-314):

Free Download Simulation and the Monte Carlo Method: Solutions Manual to Accompany, Second Edition By Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev, Rueven Y. Rubinstein(auth.)
2008 | 186 Pages | ISBN: 0470258799 | PDF | 8 MB
This accessible new edition explores the major topics in Monte Carlo simulation Simulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo Variance reduction techniques such as the transform likelihood ratio method and the screening method The score function method for sensitivity analysis The stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization The cross-entropy method to rare events estimation and combinatorial optimization Application of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy method An extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems, and sample MATLAB® programs. Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method. Content: Chapter 1 Preliminaries (pages xi-7): Chapter 2 Random Number, Random Variable, and Stochastic Process Generation (pages 9-13): Chapter 3 Simulation of Discrete?Event Systems (pages 15-17): Chapter 4 Statistical Analysis of Discrete?Event Systems (pages 19-23): Chapter 5 Controlling the Variance (pages 25-30): Chapter 6 Markov Chain Monte Carlo (pages 31-36): Chapter 7 Sensitivity Analysis and Monte Carlo Optimization (pages 37-40): Chapter 8 The Cross?Entropy Method (pages 41-46): Chapter 9 Counting via Monte Carlo (pages 47-49): Chapter 10 Appendix (pages 51-52): Chapter 11 Preliminaries (pages 53-68): Chapter 12 Random Number, Random Variable, and Stochastic Process Generation (pages 69-83): Chapter 13 Simulation of Discrete?Event Systems (pages 85-94): Chapter 14 Statistical Analysis of Discrete?Event Systems (pages 95-103): Chapter 15 Controlling the Variance (pages 105-121): Chapter 16 Markov Chain Monte Carlo (pages 123-144): Chapter 17 Sensitivity Analysis and Monte Carlo Optimization (pages 145-150): Chapter 18 The Cross?Entropy Method (pages 151-175): Chapter 19 Counting via Monte Carlo (pages 177-186): Chapter 20 Appendix (pages 187-188):

Free Download Carlo Tresca: Portrait of a Rebel By Nunzio Pernicone
2005 | 384 Pages | ISBN: 1403964785 | PDF | 4 MB
This biography reveals the life of one of the American left’s most controversial and charismatic figures in the early twentieth century: anarchist, activist, and revolutionary Carlo Tresca. Emigrating to America at age twenty-five from Italy, Tresca became a leader in the fight for workers’ rights, alongside activists like Elizabeth Gurley Flynn and Emma Goldman. Tresca played many roles for his cause: newspaper editor, labor agitator and organizer, civil libertarian, anti-fascist, and an indomitable foe of Stalinism. He continued to fight for the rights of the oppressed until gunned down by Mafioso Carmine Galante, a crime which would never be prosecuted. This engaging book not only relates Tresca’s adventure-filled story, but brings to life the volatile world of radical politics in early twentieth century America.

Free Download Random Number Generation and Monte Carlo Methods by James E. Gentle
English | PDF | 1998 | 252 Pages | ISBN : N/A | 23.3 MB
The role of Monte Carlo methods and simulation in all of the sciences has in creased in importance during the past several years. These methods are at the heart of the rapidly developing subdisciplines of computational physics, compu tational chemistry, and the other computational sciences. The growing power of computers and the evolving simulation methodology have led to the recog nition of computation as a third approach for advancing the natural sciences, together with theory and traditional experimentation. Monte Carlo is also a fundamental tool of computational statistics. At the kernel of a Monte Carlo or simulation method is random number generation. Generation of random numbers is also at the heart of many standard statis tical methods. The random sampling required in most analyses is usually done by the computer. The computations required in Bayesian analysis have become viable because of Monte Carlo methods. This has led to much wider applications of Bayesian statistics, which, in turn, has led to development of new Monte Carlo methods and to refinement of existing procedures for random number generation.