Monte Carlo Methods in Bayesian Computation

Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as improving simulation accuracy, marginal ...

Monte Carlo Methods in Bayesian Computation

Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data, and is intended as a graduate textbook or a reference book for a one-semester course at the advanced masters or Ph.D. level. It will also serve as a useful reference for applied or theoretical researchers as well as practitioners.

Monte Carlo Strategies in Scientific Computing

This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared.

Monte Carlo Strategies in Scientific Computing

This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods.

Bayesian Computation with R

This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology.

Bayesian Computation with R

There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).

Monte Carlo Methods for Applied Scientists

Grid computing info centre (grid infoware), http://www. gridcomputing.com/. Chen, M. H. and Shao, Q. M. (2000). Monte Carlo Methods in Bayesian Computation, Springer Series in Statistics (Springer–Verlag). Chib, S. and Greenberg, ...

Monte Carlo Methods for Applied Scientists

The Monte Carlo method is inherently parallel and the extensive and rapid development in parallel computers, computational clusters and grids has resulted in renewed and increasing interest in this method. At the same time there has been an expansion in the application areas and the method is now widely used in many important areas of science including nuclear and semiconductor physics, statistical mechanics and heat and mass transfer. This book attempts to bridge the gap between theory and practice concentrating on modern algorithmic implementation on parallel architecture machines. Although a suitable text for final year postgraduate mathematicians and computational scientists it is principally aimed at the applied scientists: only a small amount of mathematical knowledge is assumed and theorem proving is kept to a minimum, with the main focus being on parallel algorithms development often to applied industrial problems. A selection of algorithms developed both for serial and parallel machines are provided. Sample Chapter(s). Chapter 1: Introduction (231 KB). Contents: Basic Results of Monte Carlo Integration; Optimal Monte Carlo Method for Multidimensional Integrals of Smooth Functions; Iterative Monte Carlo Methods for Linear Equations; Markov Chain Monte Carlo Methods for Eigenvalue Problems; Monte Carlo Methods for Boundary-Value Problems (BVP); Superconvergent Monte Carlo for Density Function Simulation by B-Splines; Solving Non-Linear Equations; Algorithmic Effciency for Different Computer Models; Applications for Transport Modeling in Semiconductors and Nanowires. Readership: Applied scientists and mathematicians.

Bayesian Theory and Applications

Bayesian analysis of binary and polychotomous response data,Journal of the American Statistical Association,88, 669–679. [2] Andrieu, C., Doucet, ... Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics), Springer.

Bayesian Theory and Applications

This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field.

Case Studies in Applied Bayesian Data Science

M.-H. Chen, Q.-M. Shao, J.G. Ibrahim, Monte Carlo Methods in Bayesian Computation. Springer Series in Statistics (Springer, New York, 2000) 14. J.A. Christen, C. Fox, Markov chain Monte Carlo using an approximation. J. Comput. Graph.

Case Studies in Applied Bayesian Data Science

Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields. While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration.

Monte Carlo Simulation Based Statistical Modeling

Monte Carlo methods in Bayesian computation. New York: Springer Series in Statistics. Cummings, J. L. (1992). Depression and Parkinson's disease: A review. The American Journal of Psychiatry, 149(4), 443–454. David, D. (2007). Dunson.

Monte Carlo Simulation Based Statistical Modeling

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.

Sequential Monte Carlo Methods in Practice

2001/280 PAGES/HARDCOVER ISBN 0-387-95259-4 STATISTICS FOR ENGINEERING AND INFORMATION SCIENCE CHRISTIAN P. ROBERT THE BAYESIAN CHOICE From Decision-Theoretic Foundations to Computational Implementation Second Edition This book ...

Sequential Monte Carlo Methods in Practice

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.

Advances in Geophysics

Monte Carlo Methods in Bayesian Computation. Springer Series in Statistics. Springer-Verlag, New York. Chevrot, S., 2002. Optimal measurement of relative and absolute delay times by simulated annealing. Geophys. J. Int. 151, 164–171.

Advances in Geophysics

The critically acclaimed serialized review journal for over 50 years, Advances in Geophysics is a highly respected publication in the field of geophysics. Since 1952, each volume has been eagerly awaited, frequently consulted, and praised by researchers and reviewers alike. Now in its 52nd volume, it contains much material still relevant today--truly an essential publication for researchers in all fields of geophysics.

Bayesian Thinking Modeling and Computation

Objective Bayesian variable selection. Tech. Report, University of Florida, Department of Statistics. Chen, M.H., Shao, Q.M., Ibrahim, J.G. (2000). Monte Carlo Methods in Bayesian Computation. Springer Series in Statistics.

Bayesian Thinking  Modeling and Computation

This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics

Finite Mixture and Markov Switching Models

Monte Carlo Methods in Bayesian Computation. Springer Series in Statistics. New York: Springer. Chen, R. and J. S. Liu (1996). Predictive updating methods with application to Bayesian classification. Journal of the Royal Statistical ...

Finite Mixture and Markov Switching Models

The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.

Handbook of Markov Chain Monte Carlo

Efficient construction of reversible jump Markov chainMonteCarlo proposal distributions (withdiscussion). Journal of theRoyal Statistical Society, Series B, 65(1):3–55. ... Monte Carlo Methods in Bayesian Computation. Springer, New York ...

Handbook of Markov Chain Monte Carlo

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

Introducing Monte Carlo Methods with R

Approximate Bayesian computation in population genetics. ... Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society Series B, 61:265–285.

Introducing Monte Carlo Methods with R

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.

Handbook of Computational Statistics

... chain Monte Carlo methods . These methods provide a set of general recipes for sampling intractable multivariate distributions and have proved vital in the recent virtually revolutionary evolution and growth of Bayesian statistics .

Handbook of Computational Statistics

The Handbook of Computational Statistics: Concepts and Methodology is divided into four parts. It begins with an overview over the field of Computational Statistics. The second part presents several topics in the supporting field of statistical computing. Emphasis is placed on the need of fast and accurate numerical algorithms and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment. The third part focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Finally a set of selected applications like Bioinformatics, Medical Imaging, Finance and Network Intrusion Detection highlight the usefulness of computational statistics.

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.

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.

Extreme Value Modeling and Risk Analysis

Approximate Bayesian computation in evolution and ecology. ... Monte Carlo Methods in Bayesian Computation. Springer. Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Volume 208. Springer.

Extreme Value Modeling and Risk Analysis

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes, the book explains univariate extreme value mixture modeling, threshold selection in extreme value analysis, and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas, develops time series of extremes with applications from climatology, describes max-autoregressive and moving maxima models for extremes, and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies, such as composite likelihood, Bayesian inference, and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling, including financial investments, insurance and financial risk management, weather and climate disasters, clinical trials, and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology, hydrology, finance, insurance, sports, and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists.

Efficient Transient Noise Analysis in Circuit Simulation

Monte Carlo Methods in Bayesian Computation. Springer Series in Statistics. Springer, 2002. [CI07] G. Ciuprina and D. loan, editors. Scientific Computing in Electrical Engineering SCEE 2006, volume 11 of Springer Book Series ...

Efficient Transient Noise Analysis in Circuit Simulation

The current technological progress in microelectronics is driven by the desire to decrease feature sizes, increase frequencies and the need for low supply voltages. Amongst other effects the signal-to-noise ratio decreases and the transient noise analysis becomes necessary in the simulation of electronic circuits. Taking the inner electronic noise into account by means of Gaussian white noise currents, mathematical modelling leads to stochastic differential algebraic equations (SDAEs) with a large number of small noise sources. The simulation of such systems requires an efficient numerical time integration by mean-square convergent numerical methods.In this thesis, adaptive linear multi-step Maruyama schemes to solve stochastic differential equations (SDEs) and SDAEs are developed. A reliable local error estimate for systems with small noise is provided and a strategy for controlling the step-size and the number of solution paths simultaneously in one approximation is presented.Numerical experiments on industrial relevant real-life applications illustrate the theoretical findings.

Bayesian Time Series Models

Springer Verlag, 2009. M. Briers, A Doucet and S. S. Singh. ... Computational Statistics and Data Analysis, 5124526-4542, 2007. ... Central limit theorem for sequential Monte Carlo methods and its applications to Bayesian inference.

Bayesian Time Series Models

The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.

Stochastic Modelling for Systems Biology Third Edition

Handbook of Stochastic Methods for Physics, Chemistry, and the Natural Sciences. Springer series in synergetics. Springer, New York. Gentle, J. (2003). Random Number Generation and Monte Carlo Methods. Statistics and computing.

Stochastic Modelling for Systems Biology  Third Edition

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book. New in the Third Edition New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC Updated R package, including code relating to all of the new material New R package for parsing SBML models into simulatable stochastic Petri net models New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language Keeping with the spirit of earlier editions, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

Estimating Output Specific Efficiencies

Monte Carlo Methods in Bayesian Computation. Springer Series in Statistics. Springer Verlag, New York. Chib, S. (1995). Marginal likelihood from the Gibbs output. Journal of the American Statistical Association, 90(432):1313–1321.

Estimating Output Specific Efficiencies

The present book is the offspring of my Habilitation, which is the key to academic tenure in Austria. Legal requirements demand that a Ha bilitation be published and so only seeing it in print marks the real end of this biographical landmark project. From a scientific perspective I may hope to finally reach a broader audience with this book for a criti cal appraisal of the research done. Aside from objectives the book is a reflection of many years of research preceding Habilitation proper in the field of efficiency measurement. Regarding the subject matter the main intention was to fill an important remaining gap in the efficiency analysis literature. Hitherto no technique was available to estimate output-specific efficiencies in a statistically convincing way. This book closes this gap, although some desirable improvements and generalizations of the proposed estimation technique may yet be required, before it will eventually establish as standard tool for efficiency analysis. The likely audience for this book includes professional researchers, who want to enrich their tool set for applied efficiency analysis, as well as students of economics, management science or operations research, in tending to learn more about the potentials of rigorously understood efficiency analysis. But also managers or public officials potentially or dering efficiency studies should benefit from the book by learning about the extended capabilities of efficiency analysis. Just reading the intro duction may change their perception of value for money when it comes to comparative performance measurement.