Exploring Monte Carlo Methods

Exploring Monte Carlo Methods
Author: William L. Dunn,J. Kenneth Shultis
Publsiher: Elsevier
Total Pages: 398
Release: 2011-05-24
ISBN 10: 9780080930619
ISBN 13: 0080930611
Language: EN, FR, DE, ES & NL

Exploring Monte Carlo Methods Book Review:

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

Exploring Monte Carlo Methods
Author: William L. Dunn,J. Kenneth Shultis
Publsiher: Elsevier Science & Technology
Total Pages: 402
Release: 2011-04
ISBN 10: 9780444558640
ISBN 13: 0444558640
Language: EN, FR, DE, ES & NL

Exploring Monte Carlo Methods Book Review:

Handbook of Monte Carlo Methods

Handbook of Monte Carlo Methods
Author: Dirk P. Kroese,Thomas Taimre,Zdravko I. Botev
Publsiher: John Wiley & Sons
Total Pages: 772
Release: 2013-06-06
ISBN 10: 9781118014950
ISBN 13: 1118014952
Language: EN, FR, DE, ES & NL

Handbook of Monte Carlo Methods Book Review:

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.

Handbook of Monte Carlo Methods

Handbook of Monte Carlo Methods
Author: Dirk P. Kroese,Thomas Taimre,Zdravko I. Botev
Publsiher: John Wiley & Sons
Total Pages: 743
Release: 2011-03-15
ISBN 10:
ISBN 13: UCSD:31822037431095
Language: EN, FR, DE, ES & NL

Handbook of Monte Carlo Methods Book Review:

"The purpose of this handbook is to provide an accessible and comprehensive compendium of Monte Carlo techniques and related topics. It contains a mix of theory (summarized), algorithms (pseudo and actual), and applications. Since the audience is broad, the theory is kept to a minimum, this without sacrificing rigor. The book is intended to be used as an essential guide to Monte Carlo methods to quickly look up ideas, procedures, formulas, pictures, etc., rather than purely a monograph for researchers or a textbook for students. As the popularity of these methods continues to grow, and new methods are developed in rapid succession, the staggering number of related techniques, ideas, concepts and algorithms makes it difficult to maintain an overall picture of the Monte Carlo approach. This book attempts to encapsulate the emerging dynamics of this field of study"--

An Introduction to Quantum Monte Carlo Methods

An Introduction to Quantum Monte Carlo Methods
Author: Tao Pang
Publsiher: Morgan & Claypool Publishers
Total Pages: 80
Release: 2016-12-07
ISBN 10: 1681741091
ISBN 13: 9781681741093
Language: EN, FR, DE, ES & NL

An Introduction to Quantum Monte Carlo Methods Book Review:

Monte Carlo methods have been very prominent in computer simulation of various systems in physics, chemistry, biology, and materials science. This book focuses on the discussion and path-integral quantum Monte Carlo methods in many-body physics and provides a concise but complete introduction to the Metropolis algorithm and its applications in these two techniques. To explore the schemes in clarity, several quantum many-body systems are analysed and studied in detail. The book includes exercises to help digest the materials covered. It can be used as a tutorial to learn the discussion and path-integral Monte Carlo or a recipe for developing new research in the reader's own area. Two complete Java programs, one for the discussion Monte Carlo of 4^He clusters on a graphite surface and the other for the path-integral Monte Carlo of cold atoms in a potential trap, are ready for download and adoption.

Introducing Monte Carlo Methods with R

Introducing Monte Carlo Methods with R
Author: Christian Robert,George Casella
Publsiher: Springer Science & Business Media
Total Pages: 283
Release: 2010
ISBN 10: 1441915753
ISBN 13: 9781441915757
Language: EN, FR, DE, ES & NL

Introducing Monte Carlo Methods with R Book Review:

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.

Simulation and the Monte Carlo Method

Simulation and the Monte Carlo Method
Author: Reuven Y. Rubinstein,Dirk P. Kroese
Publsiher: John Wiley & Sons
Total Pages: 432
Release: 2016-10-21
ISBN 10: 1118632389
ISBN 13: 9781118632383
Language: EN, FR, DE, ES & NL

Simulation and the Monte Carlo Method Book Review:

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 exercises Simulation 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 Simulation and Resampling Methods for Social Science

Monte Carlo Simulation and Resampling Methods for Social Science
Author: Thomas M. Carsey,Jeffrey J. Harden
Publsiher: SAGE Publications
Total Pages: 304
Release: 2013-08-05
ISBN 10: 1483324923
ISBN 13: 9781483324920
Language: EN, FR, DE, ES & NL

Monte Carlo Simulation and Resampling Methods for Social Science Book Review:

Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, Monte Carlo Simulation and Resampling Methods for Social Science, by Thomas M. Carsey and Jeffrey J. Harden, examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.

Monte Carlo Simulation and Finance

Monte Carlo Simulation and Finance
Author: Don L. McLeish
Publsiher: John Wiley & Sons
Total Pages: 387
Release: 2011-09-13
ISBN 10: 1118160940
ISBN 13: 9781118160947
Language: EN, FR, DE, ES & NL

Monte Carlo Simulation and Finance Book Review:

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.

Markov Chain Monte Carlo Methods in Quantum Field Theories

Markov Chain Monte Carlo Methods in Quantum Field Theories
Author: Anosh Joseph
Publsiher: Springer Nature
Total Pages: 126
Release: 2020-04-16
ISBN 10: 3030460444
ISBN 13: 9783030460440
Language: EN, FR, DE, ES & NL

Markov Chain Monte Carlo Methods in Quantum Field Theories Book Review:

This primer is a comprehensive collection of analytical and numerical techniques that can be used to extract the non-perturbative physics of quantum field theories. The intriguing connection between Euclidean Quantum Field Theories (QFTs) and statistical mechanics can be used to apply Markov Chain Monte Carlo (MCMC) methods to investigate strongly coupled QFTs. The overwhelming amount of reliable results coming from the field of lattice quantum chromodynamics stands out as an excellent example of MCMC methods in QFTs in action. MCMC methods have revealed the non-perturbative phase structures, symmetry breaking, and bound states of particles in QFTs. The applications also resulted in new outcomes due to cross-fertilization with research areas such as AdS/CFT correspondence in string theory and condensed matter physics. The book is aimed at advanced undergraduate students and graduate students in physics and applied mathematics, and researchers in MCMC simulations and QFTs. At the end of this book the reader will be able to apply the techniques learned to produce more independent and novel research in the field.

Markov Chain Monte Carlo in Practice

Markov Chain Monte Carlo in Practice
Author: W.R. Gilks,S. Richardson,David Spiegelhalter
Publsiher: CRC Press
Total Pages: 512
Release: 1995-12-01
ISBN 10: 1482214970
ISBN 13: 9781482214970
Language: EN, FR, DE, ES & NL

Markov Chain Monte Carlo in Practice Book Review:

In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France,

Should We Risk It

Should We Risk It
Author: Daniel M. Kammen,David M. Hassenzahl
Publsiher: Princeton University Press
Total Pages: 404
Release: 2001-04-15
ISBN 10: 9780691074573
ISBN 13: 0691074577
Language: EN, FR, DE, ES & NL

Should We Risk It Book Review:

The Urantia Book, first published by Urantia Foundation in 1955, was written in the form of a revelation. The book consists of 196 papers which discuss the nature of Diety, the Trinity, the spiritual organization of the universe, the genesis, history, and destiny of humanity and on our relationship with God the Father. The final section presents a unique portrayal of the life and teachings of Jesus. The Urantia Book offers a uniquely integrated view of science, philosophy, and religion. Those who read and study it believe that the ideas presented in the book can contribute to the religious and philosophical growth of many individuals worldwide.

A Guide to Monte Carlo Simulations in Statistical Physics

A Guide to Monte Carlo Simulations in Statistical Physics
Author: David P. Landau,Kurt Binder
Publsiher: Cambridge University Press
Total Pages: 384
Release: 2000-08-17
ISBN 10: 9780521653664
ISBN 13: 0521653665
Language: EN, FR, DE, ES & NL

A Guide to Monte Carlo Simulations in Statistical Physics Book Review:

This book describes all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics and statistical mechanics, as well as in related fields, such as polymer science and lattice gauge theory. The authors give a succinct overview of simple sampling methods and develop the importance sampling method. In addition they introduce quantum Monte Carlo methods, aspects of simulations of growth phenomena and other systems far from equilibrium, and the Monte Carlo Renormalization Group approach to critical phenomena. The book includes many applications, examples, and current references, and exercises to help the reader.

Accelerating Monte Carlo methods for Bayesian inference in dynamical models

Accelerating Monte Carlo methods for Bayesian inference in dynamical models
Author: Johan Dahlin
Publsiher: Linköping University Electronic Press
Total Pages: 139
Release: 2016-03-22
ISBN 10: 9176857972
ISBN 13: 9789176857977
Language: EN, FR, DE, ES & NL

Accelerating Monte Carlo methods for Bayesian inference in dynamical models Book Review:

Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. In this thesis, we make use of Bayesian statistics to construct probabilistic models given prior information and historical data, which can be used for decision support and predictions. The main obstacle with this approach is that it often results in mathematical problems lacking analytical solutions. To cope with this, we make use of statistical simulation algorithms known as Monte Carlo methods to approximate the intractable solution. These methods enjoy well-understood statistical properties but are often computational prohibitive to employ. The main contribution of this thesis is the exploration of different strategies for accelerating inference methods based on sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). That is, strategies for reducing the computational effort while keeping or improving the accuracy. A major part of the thesis is devoted to proposing such strategies for the MCMC method known as the particle Metropolis-Hastings (PMH) algorithm. We investigate two strategies: (i) introducing estimates of the gradient and Hessian of the target to better tailor the algorithm to the problem and (ii) introducing a positive correlation between the point-wise estimates of the target. Furthermore, we propose an algorithm based on the combination of SMC and Gaussian process optimisation, which can provide reasonable estimates of the posterior but with a significant decrease in computational effort compared with PMH. Moreover, we explore the use of sparseness priors for approximate inference in over-parametrised mixed effects models and autoregressive processes. This can potentially be a practical strategy for inference in the big data era. Finally, we propose a general method for increasing the accuracy of the parameter estimates in non-linear state space models by applying a designed input signal. Borde Riksbanken höja eller sänka reporäntan vid sitt nästa möte för att nå inflationsmålet? Vilka gener är förknippade med en viss sjukdom? Hur kan Netflix och Spotify veta vilka filmer och vilken musik som jag vill lyssna på härnäst? Dessa tre problem är exempel på frågor där statistiska modeller kan vara användbara för att ge hjälp och underlag för beslut. Statistiska modeller kombinerar teoretisk kunskap om exempelvis det svenska ekonomiska systemet med historisk data för att ge prognoser av framtida skeenden. Dessa prognoser kan sedan användas för att utvärdera exempelvis vad som skulle hända med inflationen i Sverige om arbetslösheten sjunker eller hur värdet på mitt pensionssparande förändras när Stockholmsbörsen rasar. Tillämpningar som dessa och många andra gör statistiska modeller viktiga för många delar av samhället. Ett sätt att ta fram statistiska modeller bygger på att kontinuerligt uppdatera en modell allteftersom mer information samlas in. Detta angreppssätt kallas för Bayesiansk statistik och är särskilt användbart när man sedan tidigare har bra insikter i modellen eller tillgång till endast lite historisk data för att bygga modellen. En nackdel med Bayesiansk statistik är att de beräkningar som krävs för att uppdatera modellen med den nya informationen ofta är mycket komplicerade. I sådana situationer kan man istället simulera utfallet från miljontals varianter av modellen och sedan jämföra dessa mot de historiska observationerna som finns till hands. Man kan sedan medelvärdesbilda över de varianter som gav bäst resultat för att på så sätt ta fram en slutlig modell. Det kan därför ibland ta dagar eller veckor för att ta fram en modell. Problemet blir särskilt stort när man använder mer avancerade modeller som skulle kunna ge bättre prognoser men som tar för lång tid för att bygga. I denna avhandling använder vi ett antal olika strategier för att underlätta eller förbättra dessa simuleringar. Vi föreslår exempelvis att ta hänsyn till fler insikter om systemet och därmed minska antalet varianter av modellen som behöver undersökas. Vi kan således redan utesluta vissa modeller eftersom vi har en bra uppfattning om ungefär hur en bra modell ska se ut. Vi kan också förändra simuleringen så att den enklare rör sig mellan olika typer av modeller. På detta sätt utforskas rymden av alla möjliga modeller på ett mer effektivt sätt. Vi föreslår ett antal olika kombinationer och förändringar av befintliga metoder för att snabba upp anpassningen av modellen till observationerna. Vi visar att beräkningstiden i vissa fall kan minska ifrån några dagar till någon timme. Förhoppningsvis kommer detta i framtiden leda till att man i praktiken kan använda mer avancerade modeller som i sin tur resulterar i bättre prognoser och beslut.

Introduction to Molecular Microsimulation for Colloidal Dispersions

Introduction to Molecular Microsimulation for Colloidal Dispersions
Author: A. Satoh
Publsiher: Elsevier
Total Pages: 364
Release: 2003-06-20
ISBN 10: 9780080534947
ISBN 13: 0080534945
Language: EN, FR, DE, ES & NL

Introduction to Molecular Microsimulation for Colloidal Dispersions Book Review:

Introduction to Molecular-Microsimulation for Colloidal Dispersions provides an introduction to molecular-microsimulation methods for colloidal dispersions and is suitable for both self-study and reference. It provides the reader with a systematic understanding of the theoretical background to simulation methods, together with a wide range of practical skills for developing computational programs. Exercises are included at the end of each chapter to further assist the understanding of the subjects addressed. Provides the reader with the theoretical background to molecular-microsimulation methods Suitable for both self-study and reference Aids the reader in developing programs to meet their own requirements

Monte Carlo Methods and Models in Finance and Insurance

Monte Carlo Methods and Models in Finance and Insurance
Author: Ralf Korn,Elke Korn,Gerald Kroisandt
Publsiher: CRC Press
Total Pages: 484
Release: 2010-02-26
ISBN 10: 9781420076196
ISBN 13: 1420076191
Language: EN, FR, DE, ES & NL

Monte Carlo Methods and Models in Finance and Insurance Book Review:

Offering a unique balance between applications and calculations, Monte Carlo Methods and Models in Finance and Insurance incorporates the application background of finance and insurance with the theory and applications of Monte Carlo methods. It presents recent methods and algorithms, including the multilevel Monte Carlo method, the statistical Romberg method, and the Heath–Platen estimator, as well as recent financial and actuarial models, such as the Cheyette and dynamic mortality models. The authors separately discuss Monte Carlo techniques, stochastic process basics, and the theoretical background and intuition behind financial and actuarial mathematics, before bringing the topics together to apply the Monte Carlo methods to areas of finance and insurance. This allows for the easy identification of standard Monte Carlo tools and for a detailed focus on the main principles of financial and insurance mathematics. The book describes high-level Monte Carlo methods for standard simulation and the simulation of stochastic processes with continuous and discontinuous paths. It also covers a wide selection of popular models in finance and insurance, from Black–Scholes to stochastic volatility to interest rate to dynamic mortality. Through its many numerical and graphical illustrations and simple, insightful examples, this book provides a deep understanding of the scope of Monte Carlo methods and their use in various financial situations. The intuitive presentation encourages readers to implement and further develop the simulation methods.

Markov Chain Monte Carlo

Markov Chain Monte Carlo
Author: Dani Gamerman,Hedibert F. Lopes
Publsiher: CRC Press
Total Pages: 342
Release: 2006-05-10
ISBN 10: 148229642X
ISBN 13: 9781482296426
Language: EN, FR, DE, ES & NL

Markov Chain Monte Carlo Book Review:

While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration. Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.

Introductory Econometrics

Introductory Econometrics
Author: Humberto Barreto,Frank Howland
Publsiher: Cambridge University Press
Total Pages: 774
Release: 2006
ISBN 10: 9780521843195
ISBN 13: 0521843197
Language: EN, FR, DE, ES & NL

Introductory Econometrics Book Review:

This accessible textbook and supporting web site use Excel (R) to teach introductory econometrics.

Inverse Problem Theory and Methods for Model Parameter Estimation

Inverse Problem Theory and Methods for Model Parameter Estimation
Author: Albert Tarantola
Publsiher: SIAM
Total Pages: 342
Release: 2005-01-01
ISBN 10: 9780898717921
ISBN 13: 0898717922
Language: EN, FR, DE, ES & NL

Inverse Problem Theory and Methods for Model Parameter Estimation Book Review:

While the prediction of observations is a forward problem, the use of actual observations to infer the properties of a model is an inverse problem. Inverse problems are difficult because they may not have a unique solution. The description of uncertainties plays a central role in the theory, which is based on probability theory. This book proposes a general approach that is valid for linear as well as for nonlinear problems. The philosophy is essentially probabilistic and allows the reader to understand the basic difficulties appearing in the resolution of inverse problems. The book attempts to explain how a method of acquisition of information can be applied to actual real-world problems, and many of the arguments are heuristic.

Simulating Data with SAS

Simulating Data with SAS
Author: Rick Wicklin
Publsiher: SAS Institute
Total Pages: 354
Release: 2013
ISBN 10: 1612903320
ISBN 13: 9781612903323
Language: EN, FR, DE, ES & NL

Simulating Data with SAS Book Review:

Data simulation is a fundamental technique in statistical programming and research. Rick Wicklin's Simulating Data with SAS brings together the most useful algorithms and the best programming techniques for efficient data simulation in an accessible how-to book for practicing statisticians and statistical programmers. This book discusses in detail how to simulate data from common univariate and multivariate distributions, and how to use simulation to evaluate statistical techniques. It also covers simulating correlated data, data for regression models, spatial data, and data with given moments. It provides tips and techniques for beginning programmers, and offers libraries of functions for advanced practitioners. As the first book devoted to simulating data across a range of statistical applications, Simulating Data with SAS is an essential tool for programmers, analysts, researchers, and students who use SAS software.SAS Products and Releases: Base SAS: 9.3 SAS/ETS: 9.3 SAS/IML: 9.3 SAS/STAT: 9.3 Operating Systems: All