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Almost everyone is familiar with Monte Carlo's association with gambling, and its famous Casino. Many may also have come across the Monte Carlo fallacy, so-called after the Casino's roulette wheel ball fell on black 26th times in a row, costing players, who believed that the law of averages made such streaks impossible, millions of dollars. However, the Casino also lends its name to a tool of statistical forecasting, the Monte Carlo simulation, used to model the probability of uncertain outcomes that cannot be easily predicted from mathematical equations. This book provides a detailed account for how aspiring sports bettors can use a Monte Carlo simulation to improve the quality, and hopefully profitability, of their betting, and in doing so unravels the mystery of probability and variance that lies at the heart of all gambling.

'Adrian has a unique gift for understanding drivers and racing cars. He is ultra competitive but never forgets to have fun. An immensely likeable man.' Damon HillThe world’s foremost designer in Formula One, Adrian Newey OBE is arguably one of Britain’s greatest engineers and this is his fascinating, powerful memoir.How to Build a Car explores the story of Adrian’s unrivalled 36-year career in Formula One through the prism of the cars he has designed, the drivers he has worked alongside and the races in which he’s been involved.A true engineering genius, even in adolescence Adrian’s thoughts naturally emerged in shape and form – he began sketching his own car designs at the age of 12 and took a welding course in his school summer holidays. From his early career in IndyCar racing and on to his unparalleled success in Formula One, we learn in comprehensive, engaging and highly entertaining detail how a car actually works. Adrian has designed for the likes of Mario Andretti, Nigel Mansell, Alain Prost, Damon Hill, David Coulthard, Mika Hakkinen, Mark Webber, Sebastian Vettel and Max Verstappen, always with a shark-like purity of purpose: to make the car go faster. And while his career has been marked by unbelievable triumphs, there have also been deep tragedies; most notably Ayrton Senna’s death during his time at Williams in 1994.Beautifully illustrated with never-before-seen drawings, How to Build a Car encapsulates, through Adrian’s remarkable life story, precisely what makes Formula One so thrilling – its potential for the total synchronicity of man and machine, the perfect combination of style, efficiency and speed.

This mini pocket Nice, Cannes and Monte Carlo is perfect for travellers looking for essential information in a compact format. Written by Rough Guides' experts, it provides key details on must-see places, top attractions, and curated recommendations to help you make the most of your trip. With suggested itineraries, dining tips, and practical advice, it's your go-to companion for a seamless travel experience.In this Nice, Cannes and Monte Carlo guidebook, you will find:- Curated recommendations of places - expert picks of main attractions, family-friendly activities, relaxing spots, and the best photo opportunities- What's new and when to go - an overview of recent changes in the destination, plus seasonal insights and sustainable travel tips- Things not to miss in Nice, Cannes and Monte Carlo - Colline du Chateau, Mougins, Musee Matisse, La Croisette, Promenade des Anglais, Casino de Monte Carlo, Marche Forville, Palais Princier, Vieux Nice, Iles des Lerins- Tailored trip plans - three unique itineraries, including a Perfect day for a short break- Food and drink - recommendations on local specialities and the best dining experiences- What to do in Nice, Cannes and Monte Carlo - entertainment, shopping, sports, kids' activities, events, and nightlife options- Practical information - essential tips on transportation, money, healthcare, and tourist resources- Overview maps - handy maps on the inside cover flaps show Nice, Cannes and Monte Carlo and around- French section - basic vocabulary and phrases from the local language- Striking photography - stunning full-color images to inspire your journey- Designed for comfort and sustainability - a velvet soft-touch cover for a secure grip and premium uncoated paper from responsibly sourced materials- Bonus eBook - free download with purchase for added convenience- Coverage includes: Nice, Cannes, Monaco, Cagnes-sur-Mer, Mougins, The EstérelWhether you're visiting for the first time or returning for a quick getaway, this guide to Nice, Cannes and Monte Carlo is your trusted travel companion. Packed with expert insights, practical tips, and curated recommendations, it ensures a smooth and enriching trip-no matter how short your stay.

Monte Carlo Methods are among the most used, and useful, computational tools available today. They provide efficient and practical algorithms to solve a wide range of scientific and engineering problems in dozens of areas many of which are covered in this text. These include simulation, optimization, finance, statistical mechanics, birth and death processes, Bayesian inference, quadrature, gambling systems and more.This text is for students of engineering, science, economics and mathematics who want to learn about Monte Carlo methods but have only a passing acquaintance with probability theory. The probability needed to understand the material is developed within the text itself in a direct manner using Monte Carlo experiments for reinforcement. There is a prerequisite of at least one year of calculus and a semester of matrix algebra.Each new idea is carefully motivated by a realistic problem, thus leading to insights into probability theory via examples and numerical simulations. Programming exercises are integrated throughout the text as the primary vehicle for learning the material. All examples in the text are coded in Python as a representative language; the logic is sufficiently clear so as to be easily translated into any other language. Further, Python scripts for each worked example are freely accessible for each chapter. Along the way, most of the basic theory of probability is developed in order to illuminate the solutions to the questions posed. One of the strongest features of the book is the wealth of completely solved example problems. These provide the reader with a sourcebook to follow towards the solution of their own computational problems. Each chapter ends with a large collection of homework problems illustrating and directing the material. This book is suitable as a textbook for students of engineering, finance, and the sciences as well as mathematics. The problem-oriented approach makes it ideal for an applied course in basic probability as well as for a more specialized course in Monte Carlo Methods. Topics include probability distributions, probability calculations, sampling, counting combinatorial objects, Markov chains, random walks, simulated annealing, genetic algorithms, option pricing, gamblers ruin, statistical mechanics, random number generation, Bayesian Inference, Gibbs Sampling  and Monte Carlo integration.

This accessible new edition explores the major topics in Monte Carlo simulation that have arisen over the past 30 years and presents a sound foundation for problem solving Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the state-of-the-art theory, methods and applications that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century 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 importance (re-)sampling, and the transform likelihood ratio 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 for rare events estimation and combinatorial optimization, and application of Monte Carlo techniques for counting problems. An extensive range of exercises is provided at the end of each chapter, as well as a generous sampling of applied examples.The Third Edition features a new chapter on the highly versatile splitting method, with applications to rare-event estimation, counting, sampling, and optimization. A second new chapter introduces the stochastic enumeration method, which is a new fast sequential Monte Carlo method for tree search. In addition, the Third Edition features new material on:• Random number generation, including multiple-recursive generators and the Mersenne Twister• Simulation of Gaussian processes, Brownian motion, and diffusion processes• Multilevel Monte Carlo method• New enhancements of the cross-entropy (CE) method, including the “improved” CE method, which uses sampling from the zero-variance distribution to find the optimal importance sampling parameters• Over 100 algorithms in modern pseudo code with flow control• Over 25 new exercisesSimulation and the Monte Carlo Method, Third Edition is an excellent text for upper-undergraduate and beginning graduate courses in stochastic 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.Reuven Y. Rubinstein, DSc, was Professor Emeritus in the Faculty of Industrial Engineering and Management at Technion-Israel Institute of Technology. He served as a consultant at numerous large-scale organizations, such as IBM, Motorola, and NEC. The author of over 100 articles and six books, Dr. Rubinstein was also the inventor of the popular score-function method in simulation analysis and generic cross-entropy methods for combinatorial optimization and counting.Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics in the School of Mathematics and Physics of The University of Queensland, Australia. He has published over 100 articles and four books in a wide range of areas in applied probability and statistics, including Monte Carlo methods, cross-entropy, randomized algorithms, tele-traffic c theory, reliability, computational statistics, applied probability, and stochastic modeling.

Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component of the standard set of techniques used by statisticians. This new edition has been revised towards a coherent and flowing coverage of these simulation techniques, with incorporation of the most recent developments in the field. In particular, the introductory coverage of random variable generation has been totally revised, with many concepts being unified through a fundamental theorem of simulation There are five completely new chapters that cover Monte Carlo control, reversible jump, slice sampling, sequential Monte Carlo, and perfect sampling. There is a more in-depth coverage of Gibbs sampling, which is now contained in three consecutive chapters. The development of Gibbs sampling starts with slice sampling and its connection with the fundamental theorem of simulation, and builds up to two-stage Gibbs sampling and its theoretical properties. A third chapter covers the multi-stage Gibbs sampler and its variety of applications. Lastly, chapters from the previous edition have been revised towards easier access, with the examples getting more detailed coverage. This textbook is intended for a second year graduate course, but will also be useful to someone who either wants to apply simulation techniques for the resolution of practical problems or wishes to grasp the fundamental principles behind those methods. The authors do not assume familiarity with Monte Carlo techniques (such as random variable generation), with computer programming, or with any Markov chain theory (the necessary concepts are developed in Chapter 6). A solutions manual, which covers approximately 40% of the problems, is available for instructors who require the book for a course. Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at Université Paris Dauphine, France. He is also Head of the Statistics Laboratoryat the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris, and Adjunct Professor at Ecole Polytechnique. He has written three other books and won the 2004 DeGroot Prize for The Bayesian Choice, Second Edition, Springer 2001. He also edited Discretization and MCMC Convergence Assessment, Springer 1998. He has served as associate editor for the Annals of Statistics, Statistical Science and the Journal of the American Statistical Association. He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the Société de Statistique de Paris in 1995. George Casella is Distinguished Professor and Chair, Department of Statistics, University of Florida. He has served as the Theory and Methods Editor of the Journal of the American Statistical Association and Executive Editor of Statistical Science. He has authored three other textbooks: Statistical Inference, Second Edition, 2001, with Roger L. Berger; Theory of Point Estimation, 1998, with Erich Lehmann; and Variance Components, 1992, with Shayle R. Searle and Charles E. McCulloch. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected fellow of the International Statistical Institute.

The application of quantitative reliability evaluation in electric power sys­ tems has now evolved to the point at which most utilities use these techniques in one or more areas of their planning, design, and operation. Most of the techniques in use are based on analytical models and resulting analytical evaluation procedures. Improvements in and availability of high-speed digi­ tal computers have created the opportunity to analyze many of these prob­ lems using stochastic simulation methods and over the last decade there has been increased interest in and use made of Monte Carlo simulation in quantitative power system reliability assessment. Monte Carlo simulation is not a new concept and recorded applications have existed for at least 50 yr. However, localized high-speed computers with large-capacity storage have made Monte Carlo simulation an available and sometimes preferable option for many power system reliability applications. Monte Carlo simulation is also an integral part of a modern undergrad­ uate or graduate course on reliability evaluation of general engineering systems or specialized areas such as electric power systems. It is hoped that this textbook will help formalize the many existing applications of Monte Carlo simulation and assist in their integration in teaching programs. This book presents the basic concepts associated with Monte Carlo simulation.

My Shocking Monte Carlo Confession The playboy’s life will change – forever! He was Monaco racing royalty and I, Belle Simpson, was his housekeeper. But that evening, Alexi’s searing gaze exhilarated me. Five years later, I finally have the chance to reveal my secret – Alexi’s a father! Scandal in His Majesty's Palace She could cost him his crown…Or save it! Rebellious King Valentine’s biggest scandal was the rumour of his abdication. But could enchanting horse trainer Angelique be hiding something even more shocking?

Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as "Monte Carlo." The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo. The next two chapters focus on applications in nuclear engineering, which are illustrative of uses in other fields. Five appendices are included, which provide useful information on probability distributions, general-purpose Monte Carlo codes for radiation transport, and other matters. The famous "Buffon''s needle problem" provides a unifying theme as it is repeatedly used to illustrate many features of Monte Carlo methods.This book provides the basic detail necessary to learn how to apply Monte Carlo methods and thus should be useful as a text book for undergraduate or graduate courses in numerical methods. It is written so that interested readers with only an understanding of calculus and differential equations can learn Monte Carlo on their own. Coverage of topics such as variance reduction, pseudo-random number generation, Markov chain Monte Carlo, inverse Monte Carlo, and linear operator equations will make the book useful even to experienced Monte Carlo practitioners.- Provides a concise treatment of generic Monte Carlo methods- Proofs for each chapter- Appendixes include Certain mathematical functions; Bose Einstein functions, Fermi Dirac functions, Watson functions

Exploring Monte Carlo Methods, Second Edition provides a valuable introduction to the numerical methods that have come to be known as "Monte Carlo." This unique and trusted resource for course use, as well as researcher reference, offers accessible coverage, clear explanations and helpful examples throughout. Building from the basics, the text also includes applications in a variety of fields, such as physics, nuclear engineering, finance and investment, medical modeling and prediction, archaeology, geology and transportation planning. Provides a comprehensive yet concise treatment of Monte Carlo methods Uses the famous "Buffon's needle problem" as a unifying theme to illustrate the many aspects of Monte Carlo methods Includes numerous exercises and useful appendices on: Certain mathematical functions, Bose Einstein functions, Fermi Dirac functions and Watson functions

Exploring Monte Carlo Methods, Second Edition provides a valuable introduction to the numerical methods that have come to be known as "Monte Carlo." This unique and trusted resource for course use, as well as researcher reference, offers accessible coverage, clear explanations and helpful examples throughout. Building from the basics, the text also includes applications in a variety of fields, such as physics, nuclear engineering, finance and investment, medical modeling and prediction, archaeology, geology and transportation planning.- Provides a comprehensive yet concise treatment of Monte Carlo methods- Uses the famous "Buffon''s needle problem" as a unifying theme to illustrate the many aspects of Monte Carlo methods- Includes numerous exercises and useful appendices on: Certain mathematical functions, Bose Einstein functions, Fermi Dirac functions and Watson functions

This book describes how Monte Carlo modeling methods can be applied to Electron Microscopy and Microanalysis. Computer programs for two basic types of Monte carlo simulation are developed from physical models of the electron scattering process; a Single Scattering program capable of high accuracy but requiring long computation times, and a Plural Scattering program which is less accurate but much more rapid. The programs are optimised for use on personal computers and provide a real time graphical display of the interaction. These programs are then used as the starting point for the development of programs aimed at studying particular effects in the electron microscope including backscattering, secondary electron production, EBIC and cathodo- luminescence imaging, and X- ray microanalysis. The computer code is given in a fully annotated format so that it may be readily be modified for use in specific problems. Many examples of the applications of these methods are provided, together with a complete bibliography.

Edited by a world class polymer physicist, this is a book with chapters contributed by leading experts on the various computer simulation techniques used for macromolecular materials. It describes how to use simulation to explain experimental data and gain insight into structure and dynamic properties of polymeric structures. Explanations are given on how to overcome particular challenges posed by large size and slow relaxation polymer coils. Many important applications are covered including their current reviews.

Monte Carlo methods are a class of computational algorithms for simulating the behavior of a wide range of various physical and mathematical systems (with many variables). Their utility has increased with general availability of fast computers, and new applications are continually forthcoming. The basic concepts of Monte Carlo are both simple and straightforward and rooted in statistics and probability theory, their defining characteristic being that the methodology relies on random or pseudo-random sequences of numbers. It is a technique of numerical analysis based on the approximate solution of a problem using repeated sampling experiments and observing the proportion of times a given property is satisfied. The term Monte Carlo was first used to describe calculational methods based on chance in the 1940s, but the methods themselves preceded the term by as much as a century. Quantum Monte Carlo (QMC) first appeared in 1982 and similarly was preceded by development of the related calculational methodology. The success of QMC methods over the past few decades has been remarkable, and this book will clearly demonstrate that success in its discussion of applications. For isolated molecules, the basic material of chemistry, QMC methods have produced exact solutions of the Schroedinger equation for very small systems and the most accurate solutions available for very large systems. The range of applications is impressive: folding of protein molecules, interactions in liquids, structure modeling in crystals and enzymes, quantum dots, designing heat shields and aerodynamic forms, architecture, design, business and economics, and even cinema and video games (3D modeling). This book takes a similar approach to Henry Schaefers classic book Quantum Chemistry (OUP, 1984 now a Dover edition), collecting summaries of some of the most important papers in the quantum Monte Carlo literature, tying everything together with analysis and discussion of applications. Quantum Monte Carlo is a reference book for quantum Monte Carlo applications, belonging near the desk of every quantum chemist, physicist, and a wide range of scientists and engineers across many disciplines, destined to become a classic.

Written by leading experts from around the world, Monte Carlo and Molecular Dynamics Simulations in Polymer Science comprehensively reviews the latest simulation techniques for macromolecular materials. Focusing in particular on numerous new techniques, the book offers authoritative introductions to solutions of neutral polymers and polyelectrolytes; dynamics of polymer melts, rubbers and gels, and glassy materials; thermodynamics of polymer mixing and mesophase formation, and polymers confined at interfaces and grafted to walls. Throughout, contributors offer practical advice on how to overcome the unique challenges posed by the large size and slow relaxation of polymer coils. Students and researchers in polymer chemistry, polymer physics, chemical engineering, and materials and computational science will all benefit from the cogent, step-by-step introductions contained in this important new book.

This book describes for the first time how Monte Carlo modeling methods can be applied to electron microscopy and microanalysis. Computer programs for two basic types of Monte Carlo simulation are developed from physical models of the electron scattering process--a single scattering program capable of high accuracy but requiring long computation times, and a plural scattering program which is less accurate but much more rapid. Optimized for use on personal computers, the programs provide a real time graphical display of the interaction. The programs are then used as the starting point for the development of programs aimed at studying particular effects in the electron microscope, including backscattering, secondary electron production, EBIC and cathodo-luminescence imaging, and X-ray microanalysis. The computer code is given in a fully annotated format so that it may be readily modified for specific problems. Throughout, the author includes numerous examples of how such applications can be used. Students and professionals using electron microscopes will want to read this important addition to the literature.

This book is designed to introduce graduate students and researchers to the primary methods useful for approximating integrals. The emphasis is on those methods that have been found to be of practical use, and although the focus is on approximating higher- dimensional integrals the lower-dimensional case is also covered. Included in the book are asymptotic techniques, multiple quadrature and quasi-random techniques as well as a complete development of Monte Carlo algorithms. For the Monte Carlo section importance sampling methods, variance reduction techniques and the primary Markov Chain Monte Carlo algorithms are covered. This book brings these various techniques together for the first time, and hence provides an accessible textbook and reference for researchers in a wide variety of disciplines.

This book provides an introduction to Monte Carlo simulations in classical statistical physics and is aimed both at students beginning work in the field and at more experienced researchers who wish to learn more about Monte Carlo methods. The material covered includes methods for both equilibrium and out of equilibrium systems, and common algorithms like the Metropolis and heat-bath algorithms are discussed in detail, as well as more sophisticated ones such as continuous time Monte Carlo, cluster algorithms, multigrid methods, entropic sampling and simulated tempering. Data analysis techniques are also explained starting with straightforward measurement and error-estimation techniques and progressing to topics such as the single and multiple histogram methods and finite size scaling. The last few chapters of the book are devoted to implementation issues, including discussions of such topics as lattice representations, efficient implementation of data structures, multispin coding, parallelization of Monte Carlo algorithms, and random number generation. At the end of the book the authors give a number of example programs demonstrating the applications of these techniques to a variety of well-known models.

Monte-Carlo methods is the generic term given to numerical methods that use sampling of random numbers. This text is aimed at graduate students in mathematics, physics, engineering, economics, finance, and the biosciences that are interested in using Monte-Carlo methods for the resolution of partial differential equations, transport equations, the Boltzmann equation and the parabolic equations of diffusion. It includes applied examples, particularly in mathematical finance, along with discussion of the limits of the methods and description of specific techniques used in practice for each example.This is the sixth volume in the Oxford Texts in Applied and Engineering Mathematics series, which includes texts based on taught courses that explain the mathematical or computational techniques required for the resolution of fundamental applied problems, from the undergraduate through to the graduate level. Other books in the series include: Jordan & Smith: Nonlinear Ordinary Differential Equations: An introduction to Dynamical Systems; Sobey: Introduction to Interactive Boundary Layer Theory; Scott: Nonlinear Science: Emergence and Dynamics of Coherent Structures; Tayler: Mathematical Models in Applied Mechanics; Ram-Mohan: Finite Element and Boundary Element Applications in Quantum Mechanics; Elishakoff and Ren: Finite Element Methods for Structures with Large Stochastic Variations.

Monte-Carlo methods is the generic term given to numerical methods that use sampling of random numbers. This text is aimed at graduate students in mathematics, physics, engineering, economics, finance, and the biosciences that are interested in using Monte-Carlo methods for the resolution of partial differential equations, transport equations, the Boltzmann equation and the parabolic equations of diffusion. It includes applied examples, particularly in mathematical finance, along with discussion of the limits of the methods and description of specific techniques used in practice for each example.This is the sixth volume in the Oxford Texts in Applied and Engineering Mathematics series, which includes texts based on taught courses that explain the mathematical or computational techniques required for the resolution of fundamental applied problems, from the undergraduate through to the graduate level. Other books in the series include: Jordan & Smith: Nonlinear Ordinary Differential Equations: An introduction to Dynamical Systems; Sobey: Introduction to Interactive Boundary Layer Theory; Scott: Nonlinear Science: Emergence and Dynamics of Coherent Structures; Tayler: Mathematical Models in Applied Mechanics; Ram-Mohan: Finite Element and Boundary Element Applications in Quantum Mechanics; Elishakoff and Ren: Finite Element Methods for Structures with Large Stochastic Variations.

Monte Carlo methods are a class of computational algorithms for simulating the behavior of a wide range of various physical and mathematical systems (with many variables). Their utility has increased with general availability of fast computers, and new applications are continually forthcoming. The basic concepts of Monte Carlo are both simple and straightforward and rooted in statistics and probability theory, their defining characteristic being that the methodology relies on random or pseudo-random sequences of numbers. It is a technique of numerical analysis based on the approximate solution of a problem using repeated sampling experiments and observing the proportion of times a given property is satisfied. The term Monte Carlo was first used to describe calculational methods based on chance in the 1940s, but the methods themselves preceded the term by as much as a century. Quantum Monte Carlo (QMC) first appeared in 1982 and similarly was preceded by development of the related calculational methodology. The success of QMC methods over the past few decades has been remarkable, and this book will clearly demonstrate that success in its discussion of applications. For isolated molecules, the basic material of chemistry, QMC methods have produced exact solutions of the Schroedinger equation for very small systems and the most accurate solutions available for very large systems. The range of applications is impressive: folding of protein molecules, interactions in liquids, structure modeling in crystals and enzymes, quantum dots, designing heat shields and aerodynamic forms, architecture, design, business and economics, and even cinema and video games (3D modeling). This book takes a similar approach to Henry Schaefers classic book Quantum Chemistry (OUP, 1984 now a Dover edition), collecting summaries of some of the most important papers in the quantum Monte Carlo literature, tying everything together with analysis and discussion of applications. Quantum Monte Carlo is a reference book for quantum Monte Carlo applications, belonging near the desk of every quantum chemist, physicist, and a wide range of scientists and engineers across many disciplines, destined to become a classic.

In the 1930s and '40s, the Ballets Russes de Monte Carlo toured the United States and the world, introducing many to ballet as an art form, while spreading the enduring image of the ballerina as an embodiment of feminine grace and sophistication. This sumptuous, illustrated history tells the story of the rise of modern ballet and its popularity through the life story of one of ballet's most glamorous stars, Irina Baronova (1919 - 2008), prima ballerina for the Ballets Russes de Monte Carlo and later for Ballet Theatre in New York. Drawing on letters, correspondence, oral histories and interviews, Baronova's daughter, the actress Victoria Tennant, warmly recounts Baronova's dramatic life, from her earliest aspirations to her grueling time on tour to her later years in Australia as a pioneer of the art. She begins with the Baronov family's flight from Russia during the Revolution, which led them to Romania and later Paris, where at the age of thirteen, Baronova became a star, chosen by the legendary George Balanchine to join the Ballets Russes, where she danced the lead in Swan Lake.Tennant provides an intimate account of Baronova's life as a dancer and rare behind-the-scenes stories of life on the road with the stars of the company. The wealth of spectacular photographs, a mix of archival footage and family snapshots, offer many unseen views of rehearsals, costumes, set designs, and the dancers themselves at both their most dazzling and in their most everyday. The story of Irina Baronova is also the story of the rise of ballet in America thanks to the Ballets Russes, who brought the magisterial beauty and star power of dance to big cities and small towns alike. Irina Baronova and the Ballet Russes de Monte Carlo offers a unique perspective on this history, sure to be treasured by dance patrons and aspiring stars.

The mathematical technique of Monte Carlo, as applied to the transport of sub-atomic particles, has been described in numerous reports and books since its formal development in the 1940s. Most of these instructional efforts have been directed either at the mathematical basis of the technique or at its practical application as embodied in the several large, formal computer codes available for performing Monte Carlo transport calculations. This book attempts to fill what appears to be a gap in this Monte Carlo literature between the mathematics and the software. Thus, while the mathematical basis for Monte Carlo transport is covered in some detail, emphasis is placed on the application of the technique to the solution of practical radiation transport problems. This is done by using the PC as the basic teaching tool. This book assumes the reader has a knowledge of integral calculus, neutron transport theory, and Fortran programming. It also assumes the reader has available a PC with a Fortran compiler. Any PC of reasonable size should be adequate to reproduce the examples or solve the exercises contained herein. The authors believe it is important for the reader to execute these examples and exercises, and by doing so to become accomplished at preparing appropriate software for solving radiation transport problems using Monte Carlo. The step from the software described in this book to the use of production Monte Carlo codes should be straightforward.

In Volume 1, A Monte Carlo Primer - A Practical Approach to Radiation Transport (the "Primer"), we attempt to provide a simple, convenient, and step-by-step approach to the development, basic understanding, and use of Monte Carlo methods in radiation transport. Using the PC, the Primer begins by developing basic Monte Carlo codes to solve simple transport problems, then introduces a teaching tool, the Probabilistic Framework Code (PFC), as a standard platform for assembling, testing, and executing the various Monte Carlo techniques that are presented. This second volume attempts to continue this approach by using both custom Monte Carlo codes and PFC to apply the concepts explained in the Primer to obtain solutions to the exercises given at the end of each chapter in the Primer. A relatively modest number of exercises is included in the Primer. Some ambiguity is left in the statement of many of the exercises because the intent is not to have the user write a particular, uniquely correct piece of coding that produces a specific number as a result, but rather to encourage the user to think about the problems and develop further the concepts explained in the text. Because in most cases there is more than one way to solve a Monte Carlo transport problem, we believe that working with the concepts illustrated by the exercises is more important than obtaining anyone particular solution.

Fully updated with the latest developments in the eigenvalue Monte Carlo calculations and automatic variance reduction techniques and containing an entirely new chapter on fission matrix and alternative hybrid techniques. This second edition explores the uses of the Monte Carlo method for real-world applications, explaining its concepts and limitations. Featuring illustrative examples, mathematical derivations, computer algorithms, and homework problems, it is an ideal textbook and practical guide for nuclear engineers and scientists looking into the applications of the Monte Carlo method, in addition to students in physics and engineering, and those engaged in the advancement of the Monte Carlo methods. Describes general and particle-transport-specific automated variance reduction techniques Presents Monte Carlo particle transport eigenvalue issues and methodologies to address these issues Presents detailed derivation of existing and advanced formulations and algorithms with real-world examples from the author’s research activities