First published in 2001, Causality in Macroeconomics addresses the long-standing problems of causality while taking macroeconomics seriously. The practical concerns of the macroeconomist and abstract concerns of the philosopher inform each other. Grounded in pragmatic realism, the book rejects the popular idea that macroeconomics requires microfoundations, and argues that the macroeconomy is a set of structures that are best analyzed causally. Ideas originally due to Herbert Simon and the Cowles Commission are refined and generalized to non-linear systems, particularly to the non-linear systems with cross-equation restrictions that are ubiquitous in modern macroeconomic models with rational expectations (with and without regime-switching). These ideas help to clarify philosophical as well as economic issues. The structural approach to causality is then used to evaluate more familiar approaches to causality due to Granger, LeRoy and Glymour, Spirtes, Scheines and Kelly, as well as vector autoregressions, the Lucas critique, and the exogeneity concepts of Engle, Hendry and Richard.

What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences.The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables.The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.

One of the most valuable skills in our economy is becoming increasingly rare. If you master this skill, you'll achieve extraordinary results.
Deep work is the ability to focus without distraction on a cognitively demanding task. It's a skill that allows you to quickly master complicated information and produce better results in less time. Deep work will make you better at what you do and provide the sense of true fulfillment that comes from craftsmanship. In short, deep work is like a super power in our increasingly competitive twenty-first century economy. And yet, most people have lost the ability to go deep-spending their days instead in a frantic blur of e-mail and social media, not even realizing there's a better way.
In DEEP WORK, author and professor Cal Newport flips the narrative on impact in a connected age. Instead of arguing distraction is bad, he instead celebrates the power of its opposite. Dividing this book into two parts, he first makes the case that in almost any profession, cultivating a deep work ethic will produce massive benefits. He then presents a rigorous training regimen, presented as a series of four "rules," for transforming your mind and habits to support this skill.
A mix of cultural criticism and actionable advice, DEEP WORK takes the reader on a journey through memorable stories-from Carl Jung building a stone tower in the woods to focus his mind, to a social media pioneer buying a round-trip business class ticket to Tokyo to write a book free from distraction in the air-and no-nonsense advice, such as the claim that most serious professionals should quit social media and that you should practice being bored. DEEP WORK is an indispensable guide to anyone seeking focused success in a distracted world.

<b>A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.</b><br /><br />The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.<br /><br />The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Git is the version control system developed by Linus Torvalds for Linux kernel development. It took the open source world by storm since its inception in 2005, and is used by small development shops and giants like Google, Red Hat, and IBM, and of course many open source projects.
* A book by Git experts to turn you into a Git expert
* Introduces the world of distributed version control
* Shows how to build a Git development workflow
What you’ll learn
* Use Git as a programmer or a project leader.
* Become a fluent Git user.
* Use distributed features of Git to the full.
* Acquire the ability to insert Git in the development workflow.
* Migrate programming projects from other SCMs to Git.
* Learn how to extend Git.
This book is for all open source developers: you are bound to encounter it somewhere in the course of your working life. Proprietary software developers will appreciate Git’s enormous scalability, since it is used for the Linux project, which comprises thousands of developers and testers.

You've experienced the shiny, point-and-click surface of your Linux computer-now dive below and explore its depths with the power of the command line. The Linux Command Line takes you from your very first terminal keystrokes to writing full programs in Bash, the most popular Linux shell. Along the way you'll learn the timeless skills handed down by generations of gray-bearded, mouse-shunning gurus: file navigation, environment configuration, command chaining, pattern matching with regular expressions, and more. In addition to that practical knowledge, author William Shotts reveals the philosophy behind these tools and the rich heritage that your desktop Linux machine has inherited from Unix supercomputers of yore. As you make your way through the book's short, easily-digestible chapters, you'll learn how to: * Create and delete files, directories, and symlinks * Administer your system, including networking, package installation, and process management * Use standard input and output, redirection, and pipelines * Edit files with Vi, the world's most popular text editor * Write shell scripts to automate common or boring tasks * Slice and dice text files with cut, paste, grep, patch, and sed Once you overcome your initial "shell shock," you'll find that the command line is a natural and expressive way to communicate with your computer. Just don't be surprised if your mouse starts to gather dust.

Rigorously updated, with a new modular format, the second edition of Learning and Memory brings a modern perspective to the study of this key topic. Reflecting the growing importance of neuroscience in the field, it compares brain studies and behavioural approaches in human and other animal species, and is available in full-color throughout.

Ancient China collides with newfangled America in this epic tale of opium smugglers, sea pirates, and dueling clipper ships.
Brilliantly illuminating one of the least-understood areas of American history, best-selling author Eric Jay Dolin now traces our fraught relationship with China back to its roots: the unforgiving nineteenth-century seas that separated a brash, rising naval power from a battered ancient empire. It is a prescient fable for our time, one that surprisingly continues to shed light on our modern relationship with China. Indeed, the furious trade in furs, opium, and bêche-de-mer--a rare sea cucumber delicacy--might have catalyzed America's emerging economy, but it also sparked an ecological and human rights catastrophe of such epic proportions that the reverberations can still be felt today. Peopled with fascinating characters--from the "Financier of the Revolution" Robert Morris to the Chinese emperor Qianlong, who considered foreigners inferior beings--this page-turning saga of pirates and politicians, coolies and concubines becomes a must-read for any fan of Nathaniel Philbrick's Mayflower or Mark Kurlansky's Cod. Two maps, and 16 pages of color and 83 black-and-white illustrations.

这本书主要介绍系统软件的运行机制和原理，涉及在Windows和Linux两个系统平台上，一个应用程序在编译、链接和运行时刻所发生的各种事项，包括：代码指令是如何保存的，库文件如何与应用程序代码静态链接，应用程序如何被装载到内存中并开始运行，动态链接如何实现，C/C++运行库的工作原理，以及操作系统提供的系统服务是如何被调用的。每个技术专题都配备了大量图、表和代码实例，力求将复杂的机制以简洁的形式表达出来。本书最后还提供了一个小巧且跨平台的C/C++运行库MiniCRT，综合展示了与运行库相关的各种技术。
对装载、链接和库进行了深入浅出的剖析，并且辅以大量的例子和图表，可以作为计算机软件专业和其他相关专业大学本科高年级学生深入学习系统软件的参考书。同时，还可作为各行业从事软件开发的工程师、研究人员以及其他对系统软件实现机制和技术感兴趣者的自学教材。

Ben Horowitz, cofounder of Andreessen Horowitz and one of Silicon Valley's most respected and experienced entrepreneurs, offers essential advice on building and running a startup—practical wisdom for managing the toughest problems business school doesn’t cover, based on his popular ben’s blog.
While many people talk about how great it is to start a business, very few are honest about how difficult it is to run one. Ben Horowitz analyzes the problems that confront leaders every day, sharing the insights he’s gained developing, managing, selling, buying, investing in, and supervising technology companies. A lifelong rap fanatic, he amplifies business lessons with lyrics from his favorite songs, telling it straight about everything from firing friends to poaching competitors, cultivating and sustaining a CEO mentality to knowing the right time to cash in.
Filled with his trademark humor and straight talk, The Hard Thing About Hard Things is invaluable for veteran entrepreneurs as well as those aspiring to their own new ventures, drawing from Horowitz's personal and often humbling experiences.
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Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

At the close of the 1980s, the independent contributions of Yann Brenier, Mike Cullen and John Mather launched a revolution in the venerable field of optimal transport founded by G. Monge in the 18th century, which has made breathtaking forays into various other domains of mathematics ever since. The author presents a broad overview of this area, supplying complete and self-contained proofs of all the fundamental results of the theory of optimal transport at the appropriate level of generality. Thus, the book encompasses the broad spectrum ranging from basic theory to the most recent research results. PhD students or researchers can read the entire book without any prior knowledge of the field. A comprehensive bibliography with notes that extensively discuss the existing literature underlines the book's value as a most welcome reference text on this subject.