Monte Carlo Statistical Methods

We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

Monte Carlo Statistical Methods

Preface Monte Carlo statistical methods, particularly those based on Markov chains, have now matured to be part of the standard set of techniques used by statisticians. This book is intended to bring these techniques into the classroom, ...

We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

Introducing Monte Carlo Methods with R

First and foremost, it must not be confused with a programming addendum to our earlier book Monte Carlo Statistical Methods whose second edition came out in 2004. The current book has a different purpose, namely to make a general ...

This book covers the main tools used in statistical simulation from a programmer’s point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison.

Essentials of Monte Carlo Simulation

This chapter is a quick review on some of the common statistical tests that are useful in analyzing the output data from runs of a computer simulation model. This pertains when each run of the model yields a group of k unique output ...

Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. The theories presented in this text deal with systems that are too complex to solve analytically. As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. After the models are run several times, in a random sample way, the data for each output variable(s) of interest is analyzed by ordinary statistical methods. This book features 11 comprehensive chapters, and discusses such key topics as random number generators, multivariate random variates, and continuous random variates. Over 100 numerical examples are presented as part of the appendix to illustrate useful real world applications. The text also contains an easy to read presentation with minimal use of difficult mathematical concepts. Very little has been published in the area of computer Monte Carlo simulation methods, and this book will appeal to students and researchers in the fields of Mathematics and Statistics.

Monte Carlo Simulation Based Statistical Modeling

Statistical Methods in Medical Research, 16, 199–218. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing values. New York: Wiley. Liu, J. S. (2001). Monte-Carlo strategies in scientific computing.

This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.

Monte Carlo Simulation for the Pharmaceutical Industry

36. Monte Carlo Simulation for the Pharmaceutical Industry Concepts, Algorithms, and. Design and Analysis of Animal Studies in Pharmaceutical Development, Shein-Chung Chow and Jen-pei Liu Basic Statistics and Pharmaceutical Statistical ...

Helping you become a creative, logical thinker and skillful "simulator," Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies provides broad coverage of the entire drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods needed to carry out computer simulations efficiently, covers both descriptive and pseudocode algorithms that provide the basis for implementation of the simulation methods, and illustrates real-world problems through case studies. The text first emphasizes the importance of analogy and simulation using examples from a variety of areas, before introducing general sampling methods and the different stages of drug development. It then focuses on simulation approaches based on game theory and the Markov decision process, simulations in classical and adaptive trials, and various challenges in clinical trial management and execution. The author goes on to cover prescription drug marketing strategies and brand planning, molecular design and simulation, computational systems biology and biological pathway simulation with Petri nets, and physiologically based pharmacokinetic modeling and pharmacodynamic models. The final chapter explores Monte Carlo computing techniques for statistical inference. This book offers a systematic treatment of computer simulation in drug development. It not only deals with the principles and methods of Monte Carlo simulation, but also the applications in drug development, such as statistical trial monitoring, prescription drug marketing, and molecular docking.

Monte Carlo and Quasi Monte Carlo Methods 2008

39. 40. 41. 42. L'Ecuyer, P., Lécot, C., Tuffin, B.: A randomized Quasi-Monte Carlo simulation method for Markov chains. Operations Research 56(4), 958–975 (2008) Lehmann, E.L., Romano, J.P.: Testing Statistical Hypotheses, third edn.

This book represents the refereed proceedings of the Eighth International Conference on Monte Carlo (MC)and Quasi-Monte Carlo (QMC) Methods in Scientific Computing, held in Montreal (Canada) in July 2008. It covers the latest theoretical developments as well as important applications of these methods in different areas. It contains two tutorials, eight invited articles, and 32 carefully selected articles based on the 135 contributed presentations made at the conference. This conference is a major event in Monte Carlo methods and is the premiere event for quasi-Monte Carlo and its combination with Monte Carlo. This series of proceedings volumes is the primary outlet for quasi-Monte Carlo research.

Handbook of Monte Carlo Methods

Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field.

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications More and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field. The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including: Random variable and stochastic process generation Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run Discrete-event simulation Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo Estimation of derivatives and sensitivity analysis Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization The presented theoretical concepts are illustrated with worked examples that use MATLAB®, a related Web site houses the MATLAB® code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation. Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.

Monte Carlo Simulation and Finance

Praise for MONTE CARLO SIMULATION & FINANCE "Dr. McLeish's clear exposition of simulation methods and their application to a wide variety of practical financial problems, along with the enlightening exercise problems, make this text ...

Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields. Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon. This state-of-the-art book on Monte Carlo simulation methods is ideal for finance professionals and students. Order your copy today.

Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence

3 Monte Carlo simulation of a CPN and its associated multivariate distribution Let us now consider a causal probabilistic network (CPN) with an acyclic directed graph G := (V, E) with E G V × V and (u, v) # E for (v, u) e E and a family ...

In recent years it has become apparent that an important part of the theory of Artificial Intelligence is concerned with reasoning on the basis of uncertain, incomplete or inconsistent information. Classical logic and probability theory are only partially adequate for this, and a variety of other formalisms have been developed, some of the most important being fuzzy methods, possibility theory, belief function theory, non monotonic logics and modal logics. The aim of this workshop was to contribute to the elucidation of similarities and differences between the formalisms mentioned above.

A First Course in Bayesian Statistical Methods

CO], arxiv: 0808.2902 Robert CP, Casella G (2004) Monte Carlo statistical methods, 2nd edn. Springer Texts in Statistics, Springer-Verlag, New York Roberts GO, Rosenthal JS (2007) Coupling and ergodicity of adaptive Markov chain Monte ...

A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

Statistical Methods for Astronomical Data Analysis

Monte. Carlo. Simulation. The name “Monte Carlo” emerged from the name of the city “Monte Carlo”, famous for its Casino. In the casino there was a roulette where a small button was fixed at the centre of a wheel and the numbers 0–9 were ...

This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for astronomical problems, including regression techniques, along with their usefulness for data set problems related to size and dimension. Analysis of missing data is an important part of the book because of its significance for work with astronomical data. Both existing and new techniques related to dimension reduction and clustering are illustrated through examples. There is detailed coverage of applications useful for classification, discrimination, data mining and time series analysis. Later chapters explain simulation techniques useful for the development of physical models where it is difficult or impossible to collect data. Finally, coverage of the many R programs for techniques discussed makes this book a fantastic practical reference. Readers may apply what they learn directly to their data sets in addition to the data sets included by the authors.

Statistical Methods for Social Scientists

7.8 Monte Carlo Simulation of Dichotomous Dependent Variables There is one further way to illustrate the potential difficulties with using OLS to estimate equations with dichotomous dependent variables; this is with our Monte Carlo ...

The aspects of this text which we believe are novel, at least in degree, include: an effort to motivate different sections with practical examples and an empirical orientation; an effort to intersperse several easily motivated examples throughout the book and to maintain some continuity in these examples; and the extensive use of Monte Carlo simulations to demonstrate particular aspects of the problems and estimators being considered. In terms of material being presented, the unique aspects include the first chapter which attempts to address the use of empirical methods in the social sciences, the seventh chapter which considers models with discrete dependent variables and unobserved variables. Clearly these last two topics in particular are quite advanced--more advanced than material that is currently available on the subject. These last two topics are also currently experiencing rapid development and are not adequately described in most other texts.

Introduction to Statistical Methods for Financial Models

In Section 6.2, the statistical properties of some simple estimators were considered, such as the mean and standard ... In such cases, an alternative approach to studying the behavior of estimators is to use Monte Carlo simulation, ...

This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data. The ten chapters of the book fall naturally into three sections. Chapters 1 to 3 cover some basic concepts of finance, focusing on the properties of returns on an asset. Chapters 4 through 6 cover aspects of portfolio theory and the methods of estimation needed to implement that theory. The remainder of the book, Chapters 7 through 10, discusses several models for financial data, along with the implications of those models for portfolio theory and for understanding the properties of return data. The audience for the book is students majoring in Statistics and Economics as well as in quantitative fields such as Mathematics and Engineering. Readers are assumed to have some background in statistical methods along with courses in multivariate calculus and linear algebra.

An Analysis of Ramp Service Time Distributions by Monte Carlo Simulation

Perchonok, Philip A., and Sheldon L. Levy, "Application of Digit Simulation Techniques to Freeway On-Ramp Traffic Operations," Proc. Highway Research Board 39 (1960). Perchonok, K., and E. L. Seguin, "Vehicle Following Behavior: A Field ...

Statistical Methods for Physical Science

... 240 Method of moments, point estimation, 161-162 Minimum variance, 161 unbiased estimator, 161 Moments, 19 Monte Carlo experiment, 163–164 Monte Carlo integration technique, 429 Monte Carlo simulation field statistics assessment, ...

This volume of Methods of Experimental Physics provides an extensive introduction to probability and statistics in many areas of the physical sciences, with an emphasis on the emerging area of spatial statistics. The scope of topics covered is wide-ranging-the text discusses a variety of the most commonly used classical methods and addresses newer methods that are applicable or potentially important. The chapter authors motivate readers with their insightful discussions. Examines basic probability, including coverage of standard distributions, time series models, and Monte Carlo methods Describes statistical methods, including basic inference, goodness of fit, maximum likelihood, and least squares Addresses time series analysis, including filtering and spectral analysis Includes simulations of physical experiments Features applications of statistics to atmospheric physics and radio astronomy Covers the increasingly important area of modern statistical computing

Monte Carlo Simulation in Statistical Physics

Monte. Carlo. Method. The guide is structured such that we proceed from the “easy” simulation methods and algorithms to the more sophisticated. For each method the algorithms are presented by the technique of stepwise refinement.

Monte Carlo Simulation in Statistical Physics deals with the computer simulation of many-body systems in condensed-matter physics and related fields of physics, chemistry and beyond, to traffic flows, stock market fluctuations, etc.). Using random numbers generated by a computer, probability distributions are calculated, allowing the estimation of the thermodynamic properties of various systems. This book describes the theoretical background to several variants of these Monte Carlo methodsand gives a systematic presentation from which newcomers can learn to perform such simulations and to analyze their results. The fifth edition covers Classical as well as Quantum Monte Carlo methods. Furthermore a new chapter on the sampling of free energy landscapes has been added. To help students in their work a special web server has been installed to host programs and discussion groups (http://wwwcp.tphys.uni-heidelberg.de). Prof. Binder was the winner of the Berni J. Alder CECAM Award for Computational Physics 2001 as well as the Boltzmann Medal in 2007.

Fundamentals of Game Design

NOTE This method of simulating a process with a variety of random inputs is named after the famous casino at Monte Carlo. Gambling games all use random values (shuffled cards, thrown dice, and so on), but by repeated simulation a casino ...

To create a great video game, you must start with a solid game design: A well-designed game is easier to build, more entertaining, and has a better chance of succeeding in the marketplace. Here to teach you the essential skills of player-centric game design is one of the industry’s leading authorities, who offers a first-hand look into the process, from initial concept to final tuning. Now in its second edition, this updated classic reference by Ernest Adams offers a complete and practical approach to game design, and includes material on concept development, gameplay design, core mechanics, user interfaces, storytelling, and balancing. In an easy-to-follow approach, Adams analyzes the specific design challenges of all the major game genres and shows you how to apply the principles of game design to each one. You’ll learn how to: Define the challenges and actions at the heart of the gameplay. Write a high-concept document, a treatment, and a full design script. Understand the essentials of user interface design and how to define a game’s look and feel. Design for a variety of input mechanisms, including the Wii controller and multi-touch iPhone. Construct a game’s core mechanics and flow of resources (money, points, ammunition, and more). Develop appealing stories, game characters, and worlds that players will want to visit, including persistent worlds. Work on design problems with engaging end-of-chapter exercises, design worksheets, and case studies. Make your game accessible to broader audiences such as children, adult women, people with disabilities, and casual players. “Ernest Adams provides encyclopedic coverage of process and design issues for every aspect of game design, expressed as practical lessons that can be immediately applied to a design in-progress. He offers the best framework I’ve seen for thinking about the relationships between core mechanics, gameplay, and player—one that I’ve found useful for both teaching and research.” — Michael Mateas, University of California at Santa Cruz, co-creator of Façade