Econometrics With Machine Learning

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Econometrics with Machine Learning

Author : Felix Chan
Publisher : Springer Nature
ISBN 13 : 3031151496
Total Pages : 385 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Econometrics with Machine Learning by : Felix Chan

Download or read book Econometrics with Machine Learning written by Felix Chan and published by Springer Nature. This book was released on 2022-09-07 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.

Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes)

Author : Cheng Few Lee
Publisher : World Scientific
ISBN 13 : 9811202400
Total Pages : 5053 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes) by : Cheng Few Lee

Download or read book Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes) written by Cheng Few Lee and published by World Scientific. This book was released on 2020-07-30 with total page 5053 pages. Available in PDF, EPUB and Kindle. Book excerpt: This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.

Machine Learning for Econometrics and Related Topics

Author : Vladik Kreinovich
Publisher : Springer
ISBN 13 : 9783031436000
Total Pages : 0 pages
Book Rating : 4.4/5 (36 download)

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Book Synopsis Machine Learning for Econometrics and Related Topics by : Vladik Kreinovich

Download or read book Machine Learning for Econometrics and Related Topics written by Vladik Kreinovich and published by Springer. This book was released on 2024-07-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decades, machine learning techniques – especially techniques of deep learning – led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy – and, more generally, issues of fairness and discrimination. We hope that this volume will: help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning, and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.

Computer Age Statistical Inference

Author : Bradley Efron
Publisher : Cambridge University Press
ISBN 13 : 1108107958
Total Pages : 496 pages
Book Rating : 4.1/5 (81 download)

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Book Synopsis Computer Age Statistical Inference by : Bradley Efron

Download or read book Computer Age Statistical Inference written by Bradley Efron and published by Cambridge University Press. This book was released on 2016-07-21 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

The Economics of Artificial Intelligence

Author : Ajay Agrawal
Publisher : University of Chicago Press
ISBN 13 : 0226833127
Total Pages : 172 pages
Book Rating : 4.2/5 (268 download)

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Book Synopsis The Economics of Artificial Intelligence by : Ajay Agrawal

Download or read book The Economics of Artificial Intelligence written by Ajay Agrawal and published by University of Chicago Press. This book was released on 2024-03-05 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

Econometrics and Data Science

Author : Tshepo Chris Nokeri
Publisher : Apress
ISBN 13 : 9781484274330
Total Pages : 228 pages
Book Rating : 4.2/5 (743 download)

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Book Synopsis Econometrics and Data Science by : Tshepo Chris Nokeri

Download or read book Econometrics and Data Science written by Tshepo Chris Nokeri and published by Apress. This book was released on 2021-10-27 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models Who This Book Is For Beginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives

Machine-learning Techniques in Economics

Author : Atin Basuchoudhary
Publisher : Springer
ISBN 13 : 3319690140
Total Pages : 97 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Machine-learning Techniques in Economics by : Atin Basuchoudhary

Download or read book Machine-learning Techniques in Economics written by Atin Basuchoudhary and published by Springer. This book was released on 2017-12-28 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.

Data Science for Financial Econometrics

Author : Nguyen Ngoc Thach
Publisher : Springer Nature
ISBN 13 : 3030488535
Total Pages : 633 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Data Science for Financial Econometrics by : Nguyen Ngoc Thach

Download or read book Data Science for Financial Econometrics written by Nguyen Ngoc Thach and published by Springer Nature. This book was released on 2020-11-13 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.

Principles and Theory for Data Mining and Machine Learning

Author : Bertrand Clarke
Publisher : Springer Science & Business Media
ISBN 13 : 0387981357
Total Pages : 786 pages
Book Rating : 4.3/5 (879 download)

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Book Synopsis Principles and Theory for Data Mining and Machine Learning by : Bertrand Clarke

Download or read book Principles and Theory for Data Mining and Machine Learning written by Bertrand Clarke and published by Springer Science & Business Media. This book was released on 2009-07-21 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering

Big Data for Twenty-First-Century Economic Statistics

Author : Katharine G. Abraham
Publisher : University of Chicago Press
ISBN 13 : 022680125X
Total Pages : 502 pages
Book Rating : 4.2/5 (268 download)

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Book Synopsis Big Data for Twenty-First-Century Economic Statistics by : Katharine G. Abraham

Download or read book Big Data for Twenty-First-Century Economic Statistics written by Katharine G. Abraham and published by University of Chicago Press. This book was released on 2022-03-11 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.

Statistics with Julia

Author : Yoni Nazarathy
Publisher : Springer Nature
ISBN 13 : 3030709019
Total Pages : 527 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Statistics with Julia by : Yoni Nazarathy

Download or read book Statistics with Julia written by Yoni Nazarathy and published by Springer Nature. This book was released on 2021-09-04 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.

Predictive Econometrics and Big Data

Author : Vladik Kreinovich
Publisher : Springer
ISBN 13 : 3319709429
Total Pages : 788 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Predictive Econometrics and Big Data by : Vladik Kreinovich

Download or read book Predictive Econometrics and Big Data written by Vladik Kreinovich and published by Springer. This book was released on 2017-11-30 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent research on predictive econometrics and big data. Gathering edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018, its main focus is on predictive techniques – which directly aim at predicting economic phenomena; and big data techniques – which enable us to handle the enormous amounts of data generated by modern computers in a reasonable time. The book also discusses the applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that employs mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. It is therefore important to develop data processing techniques that explicitly focus on prediction. The more data we have, the better our predictions will be. As such, these techniques are essential to our ability to process huge amounts of available data.

Applied Econometrics with R

Author : Christian Kleiber
Publisher : Springer Science & Business Media
ISBN 13 : 0387773185
Total Pages : 229 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Applied Econometrics with R by : Christian Kleiber

Download or read book Applied Econometrics with R written by Christian Kleiber and published by Springer Science & Business Media. This book was released on 2008-12-10 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

Text as Data

Author : Justin Grimmer
Publisher : Princeton University Press
ISBN 13 : 0691207550
Total Pages : 360 pages
Book Rating : 4.6/5 (912 download)

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Book Synopsis Text as Data by : Justin Grimmer

Download or read book Text as Data written by Justin Grimmer and published by Princeton University Press. This book was released on 2022-03-29 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry

Classification and Regression Trees

Author : Leo Breiman
Publisher : Routledge
ISBN 13 : 135146048X
Total Pages : 370 pages
Book Rating : 4.3/5 (514 download)

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Book Synopsis Classification and Regression Trees by : Leo Breiman

Download or read book Classification and Regression Trees written by Leo Breiman and published by Routledge. This book was released on 2017-10-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions

Author : Matt Taddy
Publisher : McGraw Hill Professional
ISBN 13 : 1260452786
Total Pages : 384 pages
Book Rating : 4.2/5 (64 download)

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Book Synopsis Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions by : Matt Taddy

Download or read book Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions written by Matt Taddy and published by McGraw Hill Professional. This book was released on 2019-08-23 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling•Understand how use ML tools in real world business problems, where causation matters more that correlation•Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

Machine Learning

Author : Kevin P. Murphy
Publisher : MIT Press
ISBN 13 : 0262018020
Total Pages : 1102 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Machine Learning by : Kevin P. Murphy

Download or read book Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2012-08-24 with total page 1102 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.