Download or read book An Efficient Algorithm for Dynamic Pricing Using a Graphical Representation written by Maxime Cohen and published by . This book was released on 2020 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study a multi-period, multi-item dynamic pricing problem faced by a retailer. The objective is to maximize the total profit by choosing prices, while satisfying several business rules. The strength of our work lies in our graphical model reformulation, which allows us to use ideas from combinatorial optimization. We do not make any assumptions on the structure of the demand function. The complexity of our method depends linearly on the number of time periods but is exponential in the memory of the model (number of past prices that affect current demand) and in the number of items. We prove that the profit maximization problem is NP-hard by showing an approximation preserving reduction from the weighted Max-3-SAT problem. We next introduce the discrete reference price model which is a discretized version of the reference price model, accounting for an exponentially smoothed contribution of all past prices. We show that our problem can be solved efficiently under this model. We then approximate common demand functions using the discrete reference price model. To handle cross-item effects among multiple items, we propose to use a virtual reference price that assigns a reference price for each category of items (as opposed to a reference price for each item). To enhance the tractability of our approach, we cluster items into blocks and show how to adapt our method to include business constraints across blocks. Finally, we apply our solution approach using demand models calibrated with supermarket data and validate its practical performance.

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 Channel Strategies and Marketing Mix in a Connected World written by Saibal Ray and published by Springer Nature. This book was released on 2019-12-14 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to revisit the “traditional” interaction between channel strategies and the marketing mix in a connected world. In particular, it focuses on the following four dimensions in this context: Consumers, Products, Value Proposition and Sustainability. Keeping in mind the growing digitalization of business processes in the retail world and the move towards omni-channel retailing, the book introduces the state-of-the-art academic and practitioner studies along these dimensions that could enhance the understanding of the potential impact that new technologies and strategies can have on practice in the near future. When launching a new product/service to market, firms usually consider various components of the marketing mix to influence consumers’ purchase behaviors, such as product design, convenience, value proposition, promotions, sustainability initiatives, etc. This mix varies depending on the specific channel and consumer niche that the firm is targeting. But this book shows how channel strategy also influences the effectiveness in utilizing the marketing mix to attract potential customers.

Download or read book Algorithmic Pricing Based on Big Data A Critical Reflection written by Lukas Kern and published by . This book was released on 2020-08-21 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2020 in the subject Business economics - Customer Relationship Management, CRM, grade: 1,0, language: English, abstract: Setting the right product prices is crucial for companies and is part of their marketing mix and image. For instance, deviations from "optimal" sales prices can lead to considerable losses in revenue and margin. However, a huge amount of data affect the "optimal" price and the pricing process requires extensive manual resources. Advanced algorithms like machine learning might have the potential to overcome the aforementioned challenges with almost no manual interactions. Pricing algorithms constantly automate and optimize pricing decisions based on the available data. Besides positive one-time effects of price optimizations, algorithmic pricing enables companies to implement new pricing strategies like dynamic pricing, price personalization, and markdown pricing. This master thesis combines the results of a literature review and expert interviews to solve three questions: What is the research gap between the current state of the literature and business practice regarding the use of advanced algorithms based on big data for algorithmic pricing? What progress and insights have companies made in using algorithmic pricing? And how can algorithmic pricing be enhanced for future application? The master thesis starts by explaining the basic concepts of algorithmic pricing and relevant technologies. Therefore, the results and takeaways are useful for business managers without prior experience in this area. This master thesis then provides corporate decision makers with recommendations on what to consider for new pricing algorithms and on opportunities for future development of existing pricing algorithms.

Download or read book Proceedings of the Eighteenth Annual ACM SIAM Symposium on Discrete Algorithms written by Hal Gabow and published by Society for Industrial & Applied. This book was released on 2007 with total page 1317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discrete mathematics and graph theory, including combinatorics, combinatorial optimization and networks Preface Acknowledgments Region-Fault Tolerant Geometric Spanners, M. A. Abam, M. de Berg, M. Farshi, and J. Gudmundsson A PTAS for TSP with Neighborhoods among Fat Regions in the Plane, Joseph S. B. Mitchell Optimal Dynamic Vertical Ray Shooting in Rectilinear Planar Subdivisions, Yoav Giyora and Haim Kaplan Squarepants in a Tree: Sum of Subtree Clustering and Hyperbolic Pants Decomposition, David Eppstein A Near Linear Time Constant Factor Approximation for Euclidean Bichromatic Matching (Cost), Piotr Indyk Compacting Cuts: A New Linear Formulation for Minimum Cut, Robert D. Carr, Goran Konjevod, Greg Little, Venkatesh Natarajan, and Ojas Parekh Linear Programming Relaxations of Maxcut, Wenceslas Fernandez de la Vega and Claire Kenyon-Mathieu Near-Optimal Algorithms for Maximum Constraint Satisfaction Problems, Moses Charikar, Konstantin Makarychev, and Yury Makarychev Improved Bounds for the Symmetric Rendezvous Value on the Line, Qiaoming Han, Donglei Du, Juan Vera, and Luis F. Zuluaga Efficient Solutions to Relaxations of Combinatorial Problems with Submodular Penalties via the Lovász Extension and Non-smooth Convex Optimization, Fabián A. Chudak and Kiyohito Nagano Multiple Source Shortest Paths in a Genus g Graph, Sergio Cabello and Erin W. Chambers Obnoxious Centers in Graphs, Sergio Cabello and Günter Rote Maximum Matching in Graphs with an Excluded Minor, Raphael Yuster and Uri Zwick Faster Dynamic Matchings and Vertex Connectivity, Piotr Sankowski Efficient Algorithms for Computing All Low s-t Edge Connectivities and Related Problems, Ramesh Hariharan, Telikepalli Kavitha, and Debmalya Panigrahi Analytic Combinatorics—A Calculus of Discrete Structures, Philippe Flajolet Equilibria in Online Games,Roee Engelberg and Joseph (Seffi) Naor The Approximation Complexity of Win-Lose Games, Xi Chen, Shang-Hua Teng, and Paul Valiant Convergence to Approximate Nash Equilibria in Congestion Games, Steve Chien and Alistair Sinclair Efficient Contention Resolution Protocols for Selfish Agents, Amos Fiat, Yishay Mansour, and Uri Nadav Strong Price of Anarchy,Nir Andelman, Michal Feldman, and Yishay Mansour Better Online Buffer Management , Fei Li, Jay Sethuraman, and Clifford Stein Considering Suppressed Packets Improves Buffer Management in QoS Switches, Matthias Englert and Matthias Westermann Instability of FIFO in the Permanent Sessions Model at Arbitrarily Small Network Loads, Matthew Andrews On the Separation and Equivalence of Paging Strategies,Spyros Angelopoulos, Reza Dorrigiv, and Alejandro López-Ortiz Pull-Based Data Broadcast with Dependencies: Be Fair to Users, Not to Items,Julien Robert and Nicolas Schabanel Improved Bounds for the Online Steiner Tree Problem in Graphs of Bounded Edge-Asymmetry, Spyros Angelopoulos Minimizing Movement, Erik D. Demaine, MohammadTaghi Hajiaghayi, Hamid Mahini, Amin S. Sayedi-Roshkhar, Shayan Oveisgharan, and Morteza Zadimoghaddam Optimization Problems in Multiple-Interval Graphs, Ayelet Butman, Danny Hermelin, Moshe Lewenstein, and Dror Rawitz Approximation Algorithms via Contraction Decomposition, Erik D. Demaine, MohammadTaghi Hajiaghayi, and Bojan Mohar A 1.875–Approximation Algorithm for the Stable Marriage Problem, Kazuo Iwama, Shuichi Miyazaki, and Naoya Yamauchi Improved Algorithms for Path, Matching, and Packing Problems, Jianer Chen, Songjian Lu, Sing-Hoi Sze, and Fenghui Zhang Model-Driven Optimization Using Adaptive Probes, Sudipto Guha and Kamesh Munagala Estimating the Sortedness of a Data Stream, Parikshit Gopalan, T.S. Jayram, Robert Krauthgamer, and Ravi Kumar A Near-Optimal Algorithm for Computing the Entropy of a Stream, Amit Chakrabarti, Graham Cormode, and Andrew McGregor The Communication and Streaming Complexity of Computing the Longest Common and Increasing Subsequences, Xiaoming Sun and David P. Woodruff Efficient Aggregation Algorithms for Probabilistic Data, T.S. Jayram, Satyen Kale, and Erik Vee An Unbiased Pointing Operator for Unlabeled Structures, with Applications to Counting and Sampling, Manuel Bodirsky, Eric Fusy, Mihyun Kang, and Stefan Vigerske Approximating Entropy from Sublinear Samples, Mickey Brautbar and Alex Samorodnitsky Torpid Mixing of Local Markov Chains on 3-Colorings of the Discrete Torus, David Galvin and Dana Randall Probabilistic Analysis of Linear Programming Decoding, Constantinos Daskalakis, Alexandros G. Dimakis, Richard M. Karp, and Martin J. Wainwright Scrambling Adversarial Errors Using Few Random Bits, Optimal Information Reconciliation, and Better Private Codes, Adam Smith Deterministic Pivoting Algorithms for Constrained Ranking and Clustering Problems, Anke van Zuylen, Rajneesh Hegde, Kamal Jain, and David P. Williamson Aggregation of Partial Rankings, p-Ratings and Top-m Lists, Nir Ailon Algorithms and Incentives for Robust Ranking, Rajat Bhattacharjee and Ashish Goel Matroids, Secretary Problems, and Online Mechanisms, Moshe Babaioff, Nicole Immorlica, and Robert Kleinberg An Algebraic Algorithm for Weighted Linear Matroid Intersection, Nicholas J. A. Harvey An Elementary Construction of Constant-Degree Expanders, Noga Alon, Oded Schwartz, and Asaf Shapira The k-Orientability Thresholds for G_{n,p}, Daniel Fernholz and Vijaya Ramachandran The Random Graph Threshold for k-Orientiability and a Fast Algorithm for Optimal Multiple-Choice Allocation, Julie Anne Cain, Peter Sanders, and Nick Wormald On Extremal Subgraphs of Random Graphs, Graham Brightwell, Konstantinos Panagiotou, and Angelika Steger Online Vertex Colorings of Random Graphs without Monochromatic Subgraphs, Martin Marciniszyn and Reto Spöhel On Testable Properties in Bounded Degree Graphs, Artur Czumaj and Christian Sohler Embedding Metrics into Ultrametrics and Graphs into Spanning Trees with Constant Average Distortion, Ittai Abraham, Yair Bartal, and Ofer Neiman Approximation Algorithms for Embedding General Metrics into Trees, Mihai Badoiu, Piotr Indyk, and Anastasios Sidiropoulos Embedding into $l^2_{infty}$ Is Easy, Embedding into $l^3_{infty}$ Is NP-Complete, Jeff Edmonds Efficient Subspace Approximation Algorithms, Nariankadu D. Shyamalkumar and Kasturi Varadarajan A Divide and Conquer Algorithm for d-Dimensional Arrangement, Moses Charikar, Konstantin Makarychev, and Yury Makarychev Resilient Search Trees, Irene Finocchi, Fabrizio Grandoni, and Giuseppe F. Italiano Randomization Does Not Help Searching Predecessors, Mihai Patrascu and Mikkel Thorup Dynamic Weighted Ancestors, Tsvi Kopelowitz and Moshe Lewenstein Ultra-succinct Representation of Ordered Trees, Jesper Jansson, Kunihiko Sadakane, and Wing-Kin Sung Tree Exploration with Logarithmic Memory, Leszek Gasieniec, Andrzej Pelc, Tomasz Radzik, and Xiaohui Zhang Testing for a Theta, Maria Chudnovsky and Paul Seymour Deterministic Rendezvous, Treasure Hunts and Strongly Universal Exploration Sequences, Amnon Ta-Shma and Uri Zwick Planar Graphs Are in 1-STRING, J. Chalopin, D. Gonçalves, and P. Ochem On the Bandwidth Conjecture for 3-Colourable Graphs, Julia Böttcher, Mathias Schacht, and Anusch Taraz Sandpile Transience on the Grid is Polynomially Bounded, László Babai and Igor Gorodezky Digraph Measures: Kelly Decompositions, Games, and Orderings, Paul Hunter and Stephan Kreutzer Approximating the Spanning Star Forest Problem and Its Applications to Genomic Sequence Alignment, C. Thach Nguyen, Jian Shen, Minmei Hou, Li Sheng, Webb Miller, and Louxin Zhang Fast Elimination of Redundant Linear Equations and Reconstruction of Recombination-Free Mendelian Inheritance on a Pedigree, Jing Xiao, Lan Liu, Lirong Xia, and Tao Jiang Whole Genome Duplications, Multi-break Rearrangements, and Genome Halving Problem, Max A. Alekseyev and Pavel A. Pevzner Succinct Indexes for Strings, Binary Relations and Multi-labeled Trees, Jérémy Barbay, Meng He, J. Ian Munro, and S. Srinivasa Rao A Simple Storage Scheme for Strings Achieving Entropy Bounds, Paolo Ferragina and Rossano Venturini A Network Formation Game for Bipartite Exchange Economies, Eyal Even-Dar, Michael Kerans, and Siddharth Suri Cheap Labor Can Be Expensive, Ning Chen and Anna R. Karlin Buying Cheap Is Expensive: Hardness of Non-parametric Multi-product Pricing, Patrick Briest and Piotr Krysta Dynamic Pricing for Impatient Bidders, Nikhil Bansal, Ning Chen, Neva Cherniavsky, Atri Rudra, Baruch Schieber, and Maxim Sviridenko Designing and Learning Optimal Finite Support Auctions, Edith Elkind On Bregman Voronoi Diagrams, Frank Nielsen, Jean-Daniel Boissonnat, and Richard Nock Zone Diagrams: Existence, Uniqueness, and Algorithmic Challenge, Tetsuo Asano, Jirí Matoušek, and Takeshi Tokuyama Approximate Shortest Paths in Anisotropic Regions, Siu-Wing Cheng, Hyeon-Suk Na, Antoine Vigneron, and Yajun Wang On Bounded Leg Shortest Paths Problems, Liam Roditty and Michael Segal Counting Colors in Boxes, Haim Kaplan, Natan Rubin, Micha Sharir, and Elad Verbin Energy Efficient Online Deadline Scheduling, Ho-Leung Chan, Wun-Tat Chan, Tak-Wah Lam, Lap-Kei Lee, Kin-Sum Mak, and Prudence W. H. Wong Speed Scaling for Weighted Flow Time, Nikhil Bansal, Kirk Pruhs, and Cliff Stein Path-Independent Load Balancing with Unreliable Machines, James Aspnes, Yang Richard Yang, and Yitong Yin Layered Multicast Scheduling for the L_infinity Objective, Qingbo Cai and Vincenzo Liberatore Lower Bounds on Average-Case Delay for Video-on-Demand Broadcast Protocols, Wei-Lung Dustin Tseng and David Kirkpatrick Maximum s-t-Flow with k Crossings in $O(k^3 n log n)$ Time, Jan M. Hochstein and Karsten Weihe Matrix Scaling by Network Flow, Günter Rote and Martin Zachariasen Single Source Multiroute Flows and Cuts on Uniform Capacity Networks, Henning Bruhn, Jakub Cerný, Alexander Hall, and Petr Kolman Island Hopping and Path Colouring with Applications to WDM Network Design, Andrew McGregor and Bruce Shepherd Maximum Independent Sets in Graphs of Low Degree,

Download or read book Dynamic Pricing with Heterogeneous Patience Levels written by Ilan Lobel and published by . This book was released on 2017 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the problem of dynamic pricing in the presence of patient consumers. We call a consumer patient if she is willing to wait a certain number of periods for a lower price and will purchase as soon as the price is equal to or below her valuation. We allow for arbitrary joint distributions of patience levels and valuations. We propose an efficient dynamic programming algorithm for finding optimal pricing policies. The dynamic program requires a larger state space than its counterpart for a strategic consumers model. We find numerically that optimal policies can take the form of incomplete cyclic policies, combining features of both nested sales policies and decreasing cyclic policies.

Download or read book The Algorithm Design Manual written by Steven S Skiena and published by Springer Science & Business Media. This book was released on 2009-04-05 with total page 742 pages. Available in PDF, EPUB and Kindle. Book excerpt: This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW "war stories" relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java

Download or read book Approximate Dynamic Programming written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2007-10-05 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.

Download or read book Pricing and Revenue Optimization written by Robert Phillips and published by Stanford University Press. This book was released on 2005-08-05 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive introduction to the concepts, theories, and applications of pricing and revenue optimization. From the initial success of "yield management" in the commercial airline industry down to more recent successes of markdown management and dynamic pricing, the application of mathematical analysis to optimize pricing has become increasingly important across many different industries. But, since pricing and revenue optimization has involved the use of sophisticated mathematical techniques, the topic has remained largely inaccessible to students and the typical manager. With methods proven in the MBA courses taught by the author at Columbia and Stanford Business Schools, this book presents the basic concepts of pricing and revenue optimization in a form accessible to MBA students, MS students, and advanced undergraduates. In addition, managers will find the practical approach to the issue of pricing and revenue optimization invaluable. Solutions to the end-of-chapter exercises are available to instructors who are using this book in their courses. For access to the solutions manual, please contact [email protected].

Download or read book Proceedings of International Conference on Intelligent Vision and Computing ICIVC 2022 written by Harish Sharma and published by Springer Nature. This book was released on 2023-04-30 with total page 643 pages. Available in PDF, EPUB and Kindle. Book excerpt: The conference proceedings book is a collection of high-quality research articles in the field of intelligent vision and computing. It also serves as a forum for researchers and practitioners from both academia and industry to meet and share their expertise and experience. It provides opportunities for academicians and scientists along with professionals, policymakers, and practitioners from various fields in a global realm to present their research contributions and views, on one forum and interact with members inside and outside their own particular disciplines.

Download or read book The Making of a Fly written by P. A. Lawrence and published by Wiley-Blackwell. This book was released on 1992-04-15 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding how a multicellular animal develops from a single cell (the fertilized egg) poses one of the greatest challenges in biology today. Development from egg to adult involves the sequential expression of virtually the whole of an organism's genetic instructions both in the mother as she lays down developmental cues in the egg, and in the embryo itself. Most of our present information on the role of genes in development comes from the invertebrate fruit fly, Drosophila. The two authors of this text (amongst the foremost authorities in the world) follow the developmental process from fertilization through the primitive structural development of the body plan of the fly after cleavage into the differentiation of the variety of tissues, organs and body parts that together define the fly. The developmental processes are fully explained throughout the text in the modern language of molecular biology and genetics. This text represents the vital synthesis of the subject that many have been waiting for and it will enable many specific courses in developmental biology and molecular genetics to focus on it. It will appeali to 2nd and 3rd year students in these disciplines as well as in biochemistry, neurobiology and zoology. It will also have widespread appeal among researchers. Authored by one of the foremost authorities in the world. A unique synthesis of the developmental cycle of Drosophila - our major source of information on the role of genes in development. Designed to provide the basis of new courses in developmental biology and molecular genetics at senior undergraduate level. A lucid explanation in the modern language of the science.

Download or read book Virtual Competition written by Ariel Ezrachi and published by Harvard University Press. This book was released on 2016-11-14 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: “A fascinating book about how platform internet companies (Amazon, Facebook, and so on) are changing the norms of economic competition.” —Fast Company Shoppers with a bargain-hunting impulse and internet access can find a universe of products at their fingertips. But is there a dark side to internet commerce? This thought-provoking exposé invites us to explore how sophisticated algorithms and data-crunching are changing the nature of market competition, and not always for the better. Introducing into the policy lexicon terms such as algorithmic collusion, behavioral discrimination, and super-platforms, Ariel Ezrachi and Maurice E. Stucke explore the resulting impact on competition, our democratic ideals, our wallets, and our well-being. “We owe the authors our deep gratitude for anticipating and explaining the consequences of living in a world in which black boxes collude and leave no trails behind. They make it clear that in a world of big data and algorithmic pricing, consumers are outgunned and antitrust laws are outdated, especially in the United States.” —Science “A convincing argument that there can be a darker side to the growth of digital commerce. The replacement of the invisible hand of competition by the digitized hand of internet commerce can give rise to anticompetitive behavior that the competition authorities are ill equipped to deal with.” —Burton G. Malkiel, Wall Street Journal “A convincing case for the need to rethink competition law to cope with algorithmic capitalism’s potential for malfeasance.” —John Naughton, The Observer

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 Transportation Science written by and published by . This book was released on 2004 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

Download or read book Information Theory Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Download or read book The Oxford Handbook of Pricing Management written by Özalp Özer and published by OUP Oxford. This book was released on 2012-06-07 with total page 976 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Oxford Handbook of Pricing Management is a comprehensive guide to the theory and practice of pricing across industries, environments, and methodologies. The Handbook illustrates the wide variety of pricing approaches that are used in different industries. It also covers the diverse range of methodologies that are needed to support pricing decisions across these different industries. It includes more than 30 chapters written by pricing leaders from industry, consulting, and academia. It explains how pricing is actually performed in a range of industries, from airlines and internet advertising to electric power and health care. The volume covers the fundamental principles of pricing, such as price theory in economics, models of consumer demand, game theory, and behavioural issues in pricing, as well as specific pricing tactics such as customized pricing, nonlinear pricing, dynamic pricing, sales promotions, markdown management, revenue management, and auction pricing. In addition, there are articles on the key issues involved in structuring and managing a pricing organization, setting a global pricing strategy, and pricing in business-to-business settings.