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Author: Lucy Gordon Publisher: Harlequin ISBN: 1460366468 Category : Fiction Languages : en Pages : 184
Book Description
"Why on earth did I agree to this crazy plan?! Thismultimillionaire Jack Bullen had a proposal for me—to pose as his girlfriend so he could avoid anunwanted marriage. I said yes—it was a wholelot better than going back to being a waitress. Itsounded like fun—a free holiday in Monte Carlo—who’d say no? —But Jack is gorgeous! Like Pierce Brosnan. It’sreally hard doing all this kissing and flirting when it’sall 'pretend.' I want it to be for real! And you know—I’m beginning to think he likes me, too…"
Author: Lucy Gordon Publisher: Harlequin ISBN: 1460366468 Category : Fiction Languages : en Pages : 184
Book Description
"Why on earth did I agree to this crazy plan?! Thismultimillionaire Jack Bullen had a proposal for me—to pose as his girlfriend so he could avoid anunwanted marriage. I said yes—it was a wholelot better than going back to being a waitress. Itsounded like fun—a free holiday in Monte Carlo—who’d say no? —But Jack is gorgeous! Like Pierce Brosnan. It’sreally hard doing all this kissing and flirting when it’sall 'pretend.' I want it to be for real! And you know—I’m beginning to think he likes me, too…"
Author: Steve Brooks Publisher: CRC Press ISBN: 1420079425 Category : Mathematics Languages : en Pages : 620
Book Description
Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie
Author: Alicia A. Johnson Publisher: CRC Press ISBN: 1000529568 Category : Mathematics Languages : en Pages : 606
Book Description
Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.
Author: Thomas B. Hall Publisher: ISBN: 9781536177237 Category : Monte Carlo method Languages : en Pages : 207
Book Description
In this compilation, the authors first consider applying the Monte Carlo method to the general form of the heat equation that is used for analyzing conduction heat transfer. The Monte Carlo method is then extended to some convection heat transfer applications by representing the probabilistic interpretation of the energy equation to obtain the temperature profile.Following this, Monte Carlo Methods: History and Applications discusses the Monte Carlo methods needed for the estimation of the mean glandular dose in both digital mammography and digital breast tomosynthesis. Various breast anatomies are considered.The gradual development of the Monte Carlo method for solving problems of mathematical chemistry is considered. A comparison of various quantitative structure-property/activity relationships based on the Monte Carlo method is also presented.Lastly, the Monte Carlo technique is used to characterize the statistical distributions of received measurements in an electric energy power system, as well as to quantify the correlations among these variables. To check the numerical accuracy of the results, the point estimate algorithm is employed.
Author: Christian Robert Publisher: Springer Science & Business Media ISBN: 1441915753 Category : Computers Languages : en Pages : 297
Book Description
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.
Author: Abdo Abou Jaoudé Publisher: BoD – Books on Demand ISBN: 1839687592 Category : Science Languages : en Pages : 234
Book Description
In applied mathematics, the name Monte Carlo is given to the method of solving problems by means of experiments with random numbers. This name, after the casino at Monaco, was first applied around 1944 to the method of solving deterministic problems by reformulating them in terms of a problem with random elements, which could then be solved by large-scale sampling. But, by extension, the term has come to mean any simulation that uses random numbers. Monte Carlo methods have become among the most fundamental techniques of simulation in modern science. This book is an illustration of the use of Monte Carlo methods applied to solve specific problems in mathematics, engineering, physics, statistics, and science in general.
Author: Christopher Z. Mooney Publisher: SAGE ISBN: 9780803959439 Category : Mathematics Languages : en Pages : 116
Book Description
Aimed at researchers across the social sciences, this book explains the logic behind the Monte Carlo simulation method and demonstrates its uses for social and behavioural research.
Author: Faming Liang Publisher: John Wiley & Sons ISBN: 1119956803 Category : Mathematics Languages : en Pages : 308
Book Description
Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics. Key Features: Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems. A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants. Up-to-date accounts of recent developments of the Gibbs sampler. Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals. This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.
Author: Arnaud Doucet Publisher: Springer Science & Business Media ISBN: 1475734379 Category : Mathematics Languages : en Pages : 590
Book Description
Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.
Author: Lucy Gordon
Author: Lucy Gordon
Author: Lucy Gordon
Author: Steve Brooks
Author: Alicia A. Johnson
Author: Thomas B. Hall
Author: Christian Robert
Author: Abdo Abou Jaoudé
Author: Christopher Z. Mooney
Author: Faming Liang
Author: Arnaud Doucet