Download or read book Learning Customers Price Sensitivity Through Bayesian Updating written by Nafiseh Ghorbaniyan and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a seller who sells a product over T discrete period in the season. The arrival rate of customers is known but the parameter of customer sensitivity to price is unknown. We assume that the seller has a prior belief in this parameter, which is updated using the Bayesian rule. The seller's objective is to maximize total expected revenue by determining the product price at the beginning of each period. We use a stochastic discrete-time dynamic programming model for both optimal dynamic pricing and demand learning. We show that in periods with high sales, using sales information to learn the demand parameter may result in an unacceptable value for this parameter. We conclude that ignoring sales information in this period is the best policy. Furthermore, we investigate the factors that intensify this situation.
Download or read book Operationalizing Dynamic Pricing Models written by Steffen Christ and published by Springer Science & Business Media. This book was released on 2011-04-02 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity.
Download or read book Bayesian Social Learning from Consumer Reviews written by Bar Ifrach and published by . This book was released on 2019 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated by the proliferation of user-generated product-review information and its widespreaduse, this note studies a market where consumers are heterogeneous in terms of their willingness-to-pay for a new product. Each consumer observes the binary reviews (like or dislike) of consumers who purchased the product in the past and uses Bayesian updating to infer the product quality. We show that the learning process is successful as long as the price is not prohibitive and therefore at least some consumers, with sufficiently high idiosyncratic willingness-to-pay, will purchase the product irrespective of their posterior quality estimate. We examine some structural properties of the dynamics of the posterior beliefs.Finally, we study the seller's pricing problem, and we show that, if the set of possible prices is finite, then a stationary optimal pricing policy exists. If putting one item on the market involves a constant cost, then under this optimal policy, learning fails with positive probability.
Download or read book Bayesian Analysis of Demand Under Block Rate Pricing written by Koji Miyawaki and published by Springer Nature. This book was released on 2019-12-16 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.
Download or read book Bayesian Statistics the Fun Way written by Will Kurt and published by No Starch Press. This book was released on 2019-07-09 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples. Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that. This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples. By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to: - How to measure your own level of uncertainty in a conclusion or belief - Calculate Bayes theorem and understand what it's useful for - Find the posterior, likelihood, and prior to check the accuracy of your conclusions - Calculate distributions to see the range of your data - Compare hypotheses and draw reliable conclusions from them Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.
Download or read book Bayesian Statistics and Marketing written by Peter E. Rossi and published by John Wiley & Sons. This book was released on 2024-09-10 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fine-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.
Download or read book Revenue Management and Pricing Analytics written by Guillermo Gallego and published by Springer. This book was released on 2019-08-14 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: “There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available.” Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft. “The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it." Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. “This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field.” Robert L. Phillips, Director of Pricing Research at Amazon “At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference.” Richard Ratliff, Research Scientist at Sabre “This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers.” Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK.
Download or read book Artificial Intelligence and Machine Learning in the Travel Industry written by Ben Vinod and published by Springer Nature. This book was released on 2023-05-26 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high. The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice.
Download or read book Missing Data in Longitudinal Studies written by Michael J. Daniels and published by CRC Press. This book was released on 2008-03-11 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ
Download or read book Modeling and Reasoning with Bayesian Networks written by Adnan Darwiche and published by Cambridge University Press. This book was released on 2009-04-06 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
Download or read book Bayesian Learning in Financial Markets Testing for the Relevance of Information Precision in Price Discovery written by Dieter Hess and published by . This book was released on 2010 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: An important claim of Bayesian learning and a standard assumption in price discovery models is that the strength of the price impact of unanticipated information depends on the precision of the news. In this paper, we test for this assumption by analyzing intra-day price responses of CBOT T-bond futures to U.S. employment announcements. By employing additional detail information besides the widely used headline figures, we extract release-specific precision measures which allow to test for the claim of Bayesian updating. We find that the price impact of more precise information is significantly stronger. The results remain stable even after controlling for an asymmetric price response to 'good' and 'bad' news.
Download or read book Optimal Learning written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2013-07-09 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.
Download or read book Bayesian Inference in the Social Sciences written by Ivan Jeliazkov and published by John Wiley & Sons. This book was released on 2014-11-04 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing 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 performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian 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.
Download or read book Elgar Encyclopedia of Pricing written by Andreas Hinterhuber and published by Edward Elgar Publishing. This book was released on 2024-07-05 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Elgar Encyclopedia of Pricing presents a holistic view of cutting-edge topics, practical insights, and global perspectives on pricing. In-depth entries cover everything from behavioral pricing and artificial intelligence to sustainability pricing strategies and dynamic online pricing.
Download or read book The Importance of Price Beliefs in Consumer Search written by Pranav Jindal and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A consumer's decision to engage in search depends on the beliefs the consumer has about an unknown product characteristic such as price. Given beliefs are rarely observed, researchers typically assume that consumers have rational expectations or update beliefs consistent with Bayesian updating. These assumptions are not only restrictive, but additionally, do not afford the researcher, or the retailer, an opportunity to price discriminate among consumers based on heterogeneity in beliefs. We first show, through Monte Carlo experiments, how these assumptions impact estimates of search cost. Next, we design an incentive-aligned online study where subjects search over the price of a homogeneous good, and we elicit distributions of price beliefs before and after each search. Based on data collected from a nationally representative panel, we find substantial heterogeneity in prior price beliefs. We find that subjects update their beliefs in response to search outcomes, but they deviate from Bayesian updating in that they under-react to new information. Importantly, we show that (i) assuming Bayesian updating does not significantly bias search cost estimates at the aggregate level provided the researcher accounts for heterogeneous prior beliefs, (ii) eliciting heterogeneity in prior expected prices is much more important than eliciting heterogeneity in prior price uncertainty, and (iii) a retailer can increase profits through third-degree price discrimination by recognizing the heterogeneity in prior beliefs.
Download or read book Bayesian Data Analysis Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Download or read book Marketing Dynamism Sustainability Things Change Things Stay the Same written by Leroy Robinson, Jr. and published by Springer. This book was released on 2014-11-05 with total page 882 pages. Available in PDF, EPUB and Kindle. Book excerpt: Founded in 1971, the Academy of Marketing Science is an international organization dedicated to promoting timely explorations of phenomena related to the science of marketing in theory, research, and practice. Among its services to members and the community at large, the Academy offers conferences, congresses and symposia that attract delegates from around the world. Presentations from these events are published in this Proceedings series, which offers a comprehensive archive of volumes reflecting the evolution of the field. Volumes deliver cutting-edge research and insights, complimenting the Academy’s flagship journals, the Journal of the Academy of Marketing Science (JAMS) and AMS Review. Volumes are edited by leading scholars and practitioners across a wide range of subject areas in marketing science. This volume includes the full proceedings from the 2012 Academy of Marketing Science (AMS) Annual Conference held in New Orleans, Louisiana, entitled Marketing Dynamism & Sustainability: Things Change, Things Stay the Same.
