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Bayesian Analysis of Demand Under Block Rate Pricing (SpringerBriefs in Statistics)
by Koji MiyawakiThis book focuses on the structural analysis of demand under block rate pricing, a type of nonlinear pricing used mainly in public utility services. In this price system, consumers are presented with several unit prices, which makes a naive analysis biased. However, the response to the price schedule is often of interest in economics and plays an important role in policymaking. To address this issue, the book adopts a structural approach, referred to as the discrete/continuous choice approach in the literature, to develop corresponding statistical models for analysis.The resulting models are extensions of the Tobit model, a well-known statistical model in econometrics, and their hierarchical structure fits well in Bayesian methodology. Thus, the book takes the Bayesian approach and develops the Markov chain Monte Carlo method to conduct statistical inferences. The methodology derived is then applied to real-world datasets, microdata collected in Tokyo and the neighboring Chiba Prefecture, as a useful empirical analysis for prediction as well as policymaking.
Bayesian Artificial Intelligence (Chapman & Hall/CRC Computer Science & Data Analysis)
by Kevin B. Korb Ann E. NicholsonThe second edition of this bestseller provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. This edition contains a new chapter on Bayesian network classifiers and a new section on object-oriented Bayesian networks, along with new applications and case studies. It includes a new section that addresses foundational problems with causal discovery and Markov blanket discovery and a new section that covers methods of evaluating causal discovery programs. The book also offers more coverage on the uses of causal interventions to understand and reason with causal Bayesian networks. Supplemental materials are available on the book's website.
Bayesian Claims Reserving Methods in Non-life Insurance with Stan: An Introduction
by Guangyuan GaoThis book first provides a review of various aspects of Bayesian statistics. It then investigates three types of claims reserving models in the Bayesian framework: chain ladder models, basis expansion models involving a tail factor, and multivariate copula models. For the Bayesian inferential methods, this book largely relies on Stan, a specialized software environment which applies Hamiltonian Monte Carlo method and variational Bayes.
Bayesian Cost-Effectiveness Analysis with the R package BCEA (Use R!)
by Gianluca Baio Andrea Berardi Anna HeathThe book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach. Some relevant theory and introductory concepts are presented using practical examples and two running case studies. The book also describes in detail how to perform health economic evaluations using the R package BCEA (Bayesian Cost-Effectiveness Analysis). BCEA can be used to post-process the results of a Bayesian cost-effectiveness model and perform advanced analyses producing standardised and highly customisable outputs. It presents all the features of the package, including its many functions and their practical application, as well as its user-friendly web interface. The book is a valuable resource for statisticians and practitioners working in the field of health economics wanting to simplify and standardise their workflow, for example in the preparation of dossiers in support of marketing authorisation, or academic and scientific publications.
Bayesian Econometric Methods
by Gary Koop Dale J. Poirier Justin L. TobiasA new book in the Econometric Exercises series, this volume contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics. The latter half of the book contains exercises that show how these theoretical and computational skills are combined in practice, to carry out Bayesian inference in a wide variety of models commonly used by econometricians. Aimed primarily at advanced undergraduate and graduate students studying econometrics, this book may also be useful for students studying finance, marketing, agricultural economics, business economics or, more generally, any field which uses statistics. The book also comes equipped with a supporting website containing all the relevant data sets and MATLAB computer programs for solving the computational exercises.
Bayesian Econometric Methods (Econometric Exercises #7)
by Gary Koop Dale J. Poirier Justin L. Tobias Joshua ChanBayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finance and macroeconomics, including state space and unobserved components models, stochastic volatility models, ARCH, GARCH, and vector autoregressive models. The authors have also added many new exercises related to Gibbs sampling and Markov Chain Monte Carlo (MCMC) methods. The text includes regression-based and hierarchical specifications, models based upon latent variable representations, and mixture and time series specifications. MCMC methods are discussed and illustrated in detail - from introductory applications to those at the current research frontier - and MATLAB® computer programs are provided on the website accompanying the text. Suitable for graduate study in economics, the text should also be of interest to students studying statistics, finance, marketing, and agricultural economics.
Bayesian Estimation of DSGE Models
by Frank Schorfheide Edward P. HerbstDynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations.Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.
Bayesian Implementation
by T. Palfrey S. SrivastaveThe authors present a basic model of the Bayesian implementation problem and then consider its application in areas including classical pure exchange economies, public goods provision, auctions and bargaining.
Bayesian Implementation
by Thomas R. PalfreyThe implementation problem lies at the heart of a theory of institutions. Simply stated, the aim of implementation theory is to investigate in a rigorous way the relationships between outcomes in a society and how those outcomes arise. The first part of "Bayesian Implementation" presents a basic model of the Bayesian implementation problem and summarizes and explains recent developments in this branch of implementation theory. Substantive problems of interest such as public goods provision, auctions and bargaining are special cases of the model, and these are addressed in subsequent chapters.
Bayesian Inference in the Social Sciences
by Xin-She Yang Ivan JeliazkovPresents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and financeEmphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus.Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include:Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performanceState-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book's supplemental websiteInterdisciplinary coverage from well-known international scholars and practitionersBayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
Bayesian Machine Learning in Quantitative Finance: Theory and Practical Applications
by Tshilidzi Marwala Wilson Tsakane Mongwe Rendani MbuvhaThis book offers a comprehensive discussion of the Bayesian inference framework and demonstrates why this probabilistic approach is ideal for tackling the various modelling problems within quantitative finance. It demonstrates how advanced Bayesian machine learning techniques can be applied within financial engineering, investment portfolio management, insurance, municipal finance management as well as banking. The book covers a broad range of modelling approaches, including Bayesian neural networks, Gaussian processes and Markov Chain Monte Carlo methods. It also discusses the utility of Bayesian inference in quantitative finance and discusses future research goals in the applications of Bayesian machine learning in quantitative finance. Chapters are rooted in the theory of quantitative finance and machine learning while also outlining a range of practical considerations for implementing Bayesian techniques into real-world quantitative finance problems. This book is ideal for graduate researchers and practitioners at the intersection of machine learning and quantitative finance, as well as those working in computational statistics and computer science more broadly.
Bayesian Networks and Influence Diagrams: A Guide To Construction And Analysis (Information Science and Statistics #22)
by Uffe B. Kjærulff Anders L. MadsenBayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.
Bayesian Non- and Semi-parametric Methods and Applications (The Econometric and Tinbergen Institutes Lectures)
by Peter RossiThis book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
Bayesian Optimization and Data Science (SpringerBriefs in Optimization)
by Francesco Archetti Antonio CandelieriThis volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
Bayesian Process Monitoring, Control and Optimization
by Bianca M. Colosimo Enrique Del CastilloAlthough there are many Bayesian statistical books that focus on biostatistics and economics, there are few that address the problems faced by engineers. Bayesian Process Monitoring, Control and Optimization resolves this need, showing you how to oversee, adjust, and optimize industrial processes.Bridging the gap between application and dev
Bayesian Programming (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
by Pierre Bessiere Emmanuel Mazer Juan Ahuactzin Kamel MekhnachaA new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It emphasizes probability as an alternative to Boolean logic, covering new methods to build probabilistic programs for real-world applications. The book encourages readers to explore emerging areas and develop new programming languages and hardware architectures. A Python package is available on a supplementary website.
Bayesian Reasoning and Gaussian Processes for Machine Learning Applications
by Hemachandran KThis book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
Bayesian Risk Management: A Guide to Model Risk and Sequential Learning in Financial Markets (Wiley Finance)
by Matt SekerkeA risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models. Recognize the assumptions embodied in classical statistics Quantify model risk along multiple dimensions without backtesting Model time series without assuming stationarity Estimate state-space time series models online with simulation methods Uncover uncertainty in workhorse risk and asset-pricing models Embed Bayesian thinking about risk within a complex organization Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.
Bayesian Statistics and Marketing (Wiley Series In Prob And Statistics/see 1345/6,6214/5 Ser.)
by Peter E. Rossi Greg M. Allenby Sanjog MisraFine-tune your marketing research with this cutting-edge statistical toolkit Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner. Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity. Readers of the second edition of Bayesian Statistics and Marketing will also find: Discussion of Bayesian methods in text analysis and Machine Learning Updates throughout reflecting the latest research and applications Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here Extensive case studies throughout to link theory and practice Bayesian Statistics and Marketing is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.
Bayesian Statistics and New Generations: BAYSM 2018, Warwick, UK, July 2-3 Selected Contributions (Springer Proceedings in Mathematics & Statistics #296)
by Raffaele Argiento Daniele Durante Sara WadeThis book presents a selection of peer-reviewed contributions to the fourth Bayesian Young Statisticians Meeting, BAYSM 2018, held at the University of Warwick on 2-3 July 2018. The meeting provided a valuable opportunity for young researchers, MSc students, PhD students, and postdocs interested in Bayesian statistics to connect with the broader Bayesian community. The proceedings offer cutting-edge papers on a wide range of topics in Bayesian statistics, identify important challenges and investigate promising methodological approaches, while also assessing current methods and stimulating applications. The book is intended for a broad audience of statisticians, and demonstrates how theoretical, methodological, and computational aspects are often combined in the Bayesian framework to successfully tackle complex problems.
Bayonne Packaging, Inc.
by Roy D. Shapiro Paul E. MorrisonA printer and paper converter produces customized packaging used by industrial customers to deliver promotional materials, software, luxury beverages, and gift food and candy. The company specializes in creating innovative packaging solutions for its customers and providing full service from design through final delivery. Even though revenue has tripled, performance has been declining and the firm posted its first loss in over 10 years. The new VP of Operations has been hired to address operational problems resulting in cost overruns, quality problems, and late deliveries. He tours key departments including quality control and sales and visits the various work centers in the plant as he investigates the challenges in the current production process. This case can be used in a first-year MBA-level course in Operations Management. Students are asked to create a process flow diagram and perform break-even, capacity, utilization, and yield analyses before making their final recommendations for improving the firm's performance.
Bazaars, Conversations and Freedom: For a Market Culture Beyond Greed and Fear
by Rajni BakshiLong before the financial meltdown and the red alert on climate change, some far-sighted innovators diagnosed the fatal flaws in an economic system driven by greed and fear. Across the global North and South, diverse people - financial wizards, economists, business people and social activists - have been challenging the "free market" orthodoxy. They seek to recover the virtues of bazaars from the tyranny of a market model that emerged about two centuries ago. This widely praised book is a chronicle of their achievements.From Wall Street icon George Soros and VISA card designer Dee Hock we get an insider critique of the malaise. Creators of community currencies and others, like the father of microfinance, Bangladesh's Muhammad Yunus, explore how money can work differently. The doctrine of self-interest is re-examined by looking more closely at Adam Smith through the eyes of Amartya Sen. Mahatma Gandhi's concept of 'Trusteeship' gathers strength as the socially responsible investing phenomenon challenges the power of capital. Pioneers of the open source and free software movement thrive on cooperation to drive innovation. The Dalai Lama and Ela Bhatt demonstrate that it is possible to compete compassionately and to nurture a more mindful market culture.This sweeping narrative takes you from the ancient Greek agora, Indian choupal, and Native American gift culture, on to present-day Wall Street to illuminate ideas, subversive and prudent, about how the market can serve society rather than being its master. In a world exhausted by dogma, Bazaars, Conversations and Freedom is an open quest for possible futures.This fully updated and revised UK version of the 2009 Vodafone Crossword Book Award winner for non-fiction is a rare and epic narrative about those who have been quietly forging solutions and demonstrating that a more compassionate market culture is both possible and desirable.
Be * Know * Do
by Frances Hesselbein U. S. Army Richard Cavanagh General Eric K. ShinsekiThe United States Army is one of the most complex, best run organizations in the world, and central to the Army's success are strong leadership and exceptional leadership development. Army leaders must be able to act decisively and effectively in challenging situations. But the Army, despite its organizational structure, does not train leaders in a hierarchical manner. Dispersed leadership is the key to the success of the Army leadership model. Now, for the first time, you can have access to the Army's successful leadership philosophy and the principles that are outlined in Be Know Do the official Army Leadership Manual. Be Know Do makes this critical information available to civilian leaders in all sectors--business, government, and nonprofit--and gives them the guidelines they need to create an organization where leadership thrives.
Be A Free Range Human: Escape the 9-5, Create a Life You Love and Still Pay the Bills (Kogan Page Ser.)
by Marianne CantwellTrapped in a job or business that's "just not you"? Always dreaming of your next vacation or living for the weekend? Marianne Cantwell's straight-talking bestseller will help you break out of that career cage and Be A Free Range Human. It's about much more than just quitting your job and becoming your own boss. It's about life on your terms, working when, where and how you want - so you don't have to fit yourself into someone else's box to make a great income. This second edition won't just inspire you, it will give you unconventional and practical steps to: - Discover what you really want to do with your life (even if no answer has ever fully fit)- Get started in 90 days, with what you have- Create a free range career, tailor-made for you and the life you want (be it travelling the world or hanging out in your favourite café)- Stand out from the crowd and get paid well to be you Be A Free Range Human was one of the first and most popular guides to creating a custom career (without an office or a boss). Updated with new advice on how to make free range work for your personality (you don't need to be a constantly-networking extrovert. have an MBA, or get funding), this smart, energizing guide will help you cut through the noise, see your options in a new way, and get the freedom and fulfilment you crave.
Be A Great Entrepreneur: An inspiring guide to achieving success and fulfilling your business potential
by Alex MacmillanThere is no hotter topic than entrepreneurship in today's world of business and this is the perfect guide for anyone wanting to develop their entrepreneurial skills and fulfil their business potential. Containing lots of practical advice, it also features interviews with successful entrpreneurs who have a wealth of experience to share. Coverage includes the different types of entrepreneurial opportunity out there, how to maintain passion, persistence and personal drive and how to outskill your competitors. There is also a final chapter on how to maximise the value of your business for selling, should you decide to move on to other challenges or to retire. Written by a leading expert on entrepreneurship, this is a must-have for anyone looking to be the next Alan Sugar!NOT GOT MUCH TIME?One, five and ten-minute introductions to key principles to get you started.AUTHOR INSIGHTSLots of instant help with common problems and quick tips for success, based on the author's many years of experience.TEST YOURSELFTests in the book and online to keep track of your progress.EXTEND YOUR KNOWLEDGEExtra online articles at www.teachyourself.com to give you a richer understanding of entrepreneurship.FIVE THINGS TO REMEMBERQuick refreshers to help you remember the key facts.TRY THISInnovative exercises illustrate what you've learnt and how to use it.