Matrix Tricks for Linear Statistical Models

Masculinity, fast-changing and regularly declared to be in the throes of crisis, is attracting more popular and scholarly debate in China than ever before. At the same time, Chinese literature since 1989 has been characterized as brimming with countercultural ‘attitude’. This book probes the link between literary rebellion and manhood in China, showing how male writers, as they critique the outcomes of decades of market reform, also ask the same question: how best to be a man in the new postsocialist order?In this first full-length discussion of masculinity in post-1989 Chinese literature, Pamela Hunt offers a detailed analysis of four contemporary authors in particular: Zhu Wen, Feng Tang, Xu Zechen, and Han Han. In a series of insightful readings, she explores how all four writers show the same preoccupation with the figure of the man on the edges of society. Drawing on longstanding Chinese and global models of maverick and marginal masculinity, and responding to a desire to retain a

本書編譯自 Oppenheim 等人所著 Signals and Systems 第二版，內容詳實完整、解說清晰，圖文編輯和範例的選取與表達都是一流之作，堪稱「信號與系統」的經典教科書，詳讀後絕對可奠立非常厚實的學理基礎。 本書內容共分為 9 章，內容包含：信號與系統、線性非時變系統、週期信號的傅立葉級數表示、連續時間傅立葉轉換、離散時間傅立葉轉換、信號和系統的時間和頻率特性、取樣、拉普拉斯轉換和 z 轉換。 原文第 8 章的「Communication Systems」與第 11 章的「Linear Feedback Systems」分別屬於通訊與控制領域的主要課題，就「信號與系統」的學習而言是進階的應用課題，故考量篇幅以及一般大多數讀者的需求，在本次的譯著中將這兩章的內容排除。希望本譯著能讓「信號與系統」的學理更快速地被華語文的讀者吸收，進而提升學習效率。

宇宙的智慧體可以轉換能量形式， 成為佛經裡具備身形的諸佛、菩薩或護法。 所有修行過程中的念經、祈禱佛和菩薩保佑或是持咒， 都是用來轉換宇宙智慧能量， 我們應該踏實地了解佛、菩薩名號的真正意涵， 一字一句，認真感受及體驗梵字裡隱含的意思與能量。 依據量子力學，意識的振動會產生能量， 而發出音韻也是一種振動，亦會引動宇宙的能量。 誦經則同時包含了這兩種形式的振動。 本書以量子實驗的角度來分析佛教思想，說明兩者呼應之處。 在此基礎上，以《藥師經》為主要範例，介紹佛教修行之經、咒、儀軌的重要性。 除了念誦正式經文，前行儀軌能提高誦經的效能，後續儀軌則是對經典內容的總複習，皆不可輕忽！ ◆延伸學習：四合經儀軌的比較分析、量子力學雙縫實驗介紹 本書是筆者先前著作《咒語：下載宇宙能量的通關密碼》的延伸，一開始先說明了量子力學與佛教思想的連結，介紹量子力學中的科學家普朗克的母體（matrix）、惠勒的延遲選擇實驗（delayed choice experiment）、波姆的內在隱含的秩序（implicate order）、海森堡的測不準原理（uncertainty principle）等。宇宙意識以能量的形式持續振動，而諸佛和菩薩都是宇宙神聖的意識體，佛教最高層級的宇宙意識就是「無上正等正覺」，接近科學家普朗克所說的「有意識的智慧」──Matrix（母體）。 從第二單元起，正式由量子力學進入佛教世界的經、咒、儀軌。「經」是人類的智慧文字，「咒」是佛、菩薩的智慧能量。至於「儀軌」則是僧侶於寺院誦經持咒時的儀式軌範，但也可以應用於個人居家的修行。其中的儀軌在「誦經的前後」可區隔為前行儀軌與後續儀軌。 「前行儀軌」的重點是提高誦經效能的方法，同時下載佛菩薩的智慧能量，過程中將以真言咒語淨化人類及地球空間的能量，讚歎宇宙虛空的生成，凝聚其能量，再呼喚該部佛經對應的宇宙智慧體，例如《藥師經》就包含了二十三股宇宙智慧能量的擬像化。 「後續儀軌」是誦經之後透由真言、偈、讚對經典內容的總複習，〈藥師灌頂真言〉是最根本的咒語，〈消解病咒〉則是輔助的真言；〈藥師偈〉、〈藥師讚〉、〈解冤偈〉是讀經之後的心得整理；〈三皈依〉與〈迴向偈〉則是不同經典必有的單元，圍繞著感恩、懺悔與慈悲。兩者之間才是正式經文，本書以《藥師琉璃光如來本願功德經》這部經典進行說明。 《藥師經》是難得一見「經、咒、儀軌」三合一的智慧

A dark land filled with shadows and strange noises. A multiheaded beast standing guard. A new, sly ruler with a few tricks in store. A lack of Mutt Nuggets to fill Ares the pug's belly ... The Olympians of Mount Olympus Pet Center have never faced a challenge quite like this. With Zeus the Mighty trapped in his palace by an all-seeing guard dog named Cerberus, it's up to his gaggle of gods and goddesses to figure out how to rescue him. Once the Oracle speaks, the critters know they must venture deep into the belly of the vast Underworld to find the powerful relic that can free Zeus. But with so many new faces along the way, who can they trust? Will their quick thinking and teamwork be enough to save Zeus and themselves? After all, they found a way in, but can anyone really escape the Underworld? Join Zeus the overconfident hamster, Athena the wise cat, Ares the treat-loving pug, Poseidon the proud pufferfish, Demeter the loyal grasshopper, and Hermes the daring chicken on another laugh

Discover Earth's most beautiful and fascinating natural landmarks. From the spectacular granite domes of Yosemite to the reefs of the Bahama Banks and the ice sheets of the Antarctic, this is an unparalleled survey of the world's natural treasures.From the Rocky Mountains to the Great barrier Reef and everything in between, Natural Wonders of the World combines breathtaking landscape photography and illustrations with 3-D terrain models and other explanatory artworks to reveal what lies beneath the surface and explain the geological processes to show how the features were formed. Plants and animals that inhabit each environment are also included, making Natural Wonders of the World a complete celebration of our world.Produced in association with the Smithsonian Institution.

★ 前華爾街量化分析師揭露：演算法如何操控你的未來★ 大數據黑暗面全面曝光：看不見的歧視與不公平我們以為世界正變得更公平——由機器取代人類做決策，似乎能消除偏見。但真相，可能正好相反。在這個「演算法主宰」的時代，你能否錄取學校、拿到工作、申請貸款，甚至保險費率高低，都悄悄被一套看不見的數學模型決定。這些系統看似客觀，卻無法被檢驗，也無從質疑，一旦出錯，影響卻會層層放大。數學家暨資料科學家凱西．歐尼爾，以親身經驗揭開大數據的陰暗面：這些模型不只沒有消除不平等，反而強化既有的階級與歧視，讓弱勢更難翻身。本書如同一場警醒之旅，帶你看見科技背後不為人知的權力運作。NEW YORK TIMES BESTSELLER • A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric—with a new afterword“A manual for the twenty-first-century citizen . . . relevant and urgent.”—Financial TimesNATIONAL BOOK AWARD LONGLIST • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review • The Boston Globe • Wired • Fortune • Kirkus Reviews • The Guardian • Nature • On PointWe live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead t

Emerging readers will love this Level 3 Ready-to-Read value pack of six History of Fun Stuff books for the price of four! For an added bonus, take a peek inside for tips to help your child become a re

Unmask the haunted history of Halloween in this fact-tastic, nonfiction Level 3 Ready-to-Read, part of a new series all about the history of fun stuff!Did you know that hundreds of years ago, people w

論文．我國工礦安全衛生技師核心職能需求評估之研究－謝馥蔓、游逸駿、林雲卿、蔡朋　枝．低壓設備接地線帶危害電壓偵測模組雛型研製－吳啟瑞、簡佑倫、江國銘、蘇文源．利用CCD目鏡結合位相差顯微鏡進行石棉濾紙樣本鏡檢可行性探討－謝俊明．中部地區裝潢木工作業調查與暴露評估研究－莊坤遠、李聯雄、賴嘉祥、楊崒苑、　劉丞斌．A Empirical Study of the Models of OSH

本書是一部系統性探討海洋與沿岸波浪的專業著作，兼顧物理意義與統計分析，適合研究所學生、研究人員與工程實務者閱讀。內容從觀測技術談起，介紹現地量測與遙測方法，並逐步建立描述波浪的參數與統計架構。書中詳述線性波理論在外海與近岸水域的應用，說明波浪在不同水深與地形條件下的行為差異，並深入介紹 SWAN 波浪模式，協助讀者理解現代數值模擬在工程與研究中的角色。全書結構嚴謹、插圖清楚，是理解風生波浪與海岸波動不可或缺的參考書。Waves in Oceanic and Coastal Waters describes the observation, analysis and prediction of wind-generated waves in the open ocean, in shelf seas, and in coastal regions with islands, channels, tidal flats and inlets, estuaries, fjords and lagoons. Most of this richly illustrated book is devoted to the physical aspects of waves. After introducing observation techniques for waves, both at sea and from space, the book defines the parameters that characterise waves. Using basic statistical and physical concepts, the author discusses the prediction of waves in oceanic and coastal waters, first in terms of generalised observations, and then in terms of the more theoretical framework of the spectral energy balance. He gives the results of established theories and also the direction in wh

Following on from Volume I, this is the most comprehensive manual published to date covering the NLP Master Practitioner course. The authors introduce some of the newer advancements in the field and invite you to go beyond the Practitioner level and on to the Master s level where you will not only understand NLP, but you will be able to develop the very spirit of NLP. In addition, this volume also introduces the reader to: Advanced Meta-Model Distinctions, Mind-Lines, Advanced Time-Line Patterns, and Meta-Domains Systemic Models. Packed with case studies, seminar demonstrations, discussions, and trances, this volume will enable the practitioner to develop an attitude and spirit that allows them to apply the NLP Model powerfully.

隨著金融市場高度電子化，交易方式與競爭邏輯快速改變。本書從全球證券與衍生性商品市場出發，系統性說明電子交易的核心模式、基礎建設與法規環境。作者以清楚的結構，帶領讀者理解專業電子交易者如何在不同平台中運作與獲利，並分析技術革新對市場效率與風險管理的影響。作為 Wiley Trading 系列之一，本書聚焦產業第一線的實際需求，適合作為專業人士的參考工具，也能協助讀者建立對電子金融市場的整體認識。This book tackles the concepts, technical architecture, recent developments, and future opportunities in the new field of "direct access" electronic markets. The author provides a comprehensive coverage of electronic market models, exchange platforms and networks, while describing ways in which professional electronic traders make money. Professional Electronic Trading is one of the few books that deals directly with the issues that industry professionals confront every day, making it an ideal reference and training tool. David James Norman (Chicago, IL) is Director of Electronic Markets Development at the Center for Law and Financial Markets at IIT. New technology and the advent of electronic trading have opened the floodgates to both foreign and domestic markets. Traders need the wisdom of

Linear algebra is something all mathematics undergraduates and many other students, in subjects ranging from engineering to economics, have to learn. The fifth edition of this hugely successful textbook retains the quality of earlier editions while at the same time seeing numerous minor improvements and major additions. The latter include: a new chapter on singular values and singular vectors, including ways to analyze a matrix of data; a revised chapter on computing in linear algebra, with professional-level algorithms and code that can be downloaded for a variety of languages; a new section on linear algebra and cryptography; and a new chapter on linear algebra in probability and statistics. A dedicated and active website also offers solutions to exercises as well as new exercises from many different sources (e.g. practice problems, exams, development of textbook examples), plus codes in MATLAB, Julia, and Python.

In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, includi

*Extensive applications of linear algebra concepts to a variety of real-world situations are provided. Students learn how theories and concepts of linear algebra can help solve modern-day problems.*Abundant computer exercises—more extensive than any other linear algebra book available—help students visualize and discover linear algebra, and allow them to explore more realistic applications that are too computationally intensive to work out by hand. These exercises also give students experience in performing matrix computations.*Worked-out examples illustrate new concepts, making the material less abstract and helping students quickly build their understanding.*Two chapter tests for every chapter help students study for exams and get the practice they need to master the material.*A comprehensive MATLAB® appendix gives users all the information they need to use the latest version of MATLAB.

This monograph arose from lectures at the University of Oklahoma on topics related to linear algebra over commutative rings. It provides an introduction of matrix theory over commutative rings. The mo

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix no

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

1. Added material on important techniques for data mining, including regression trees and neural network models in Chapters 11 and 13.2. The Chapter on logistic regression (Chapter 14) has been extens

Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and ad

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapt

While there are numerous books on the analysis of Generalized Linear Models (GLMs), there is very little information available on how to design the experiments that will collect the data. This book wi

Statistical Methods for Overdispersed Count Data provides a review of the most recent methods and models for such data, including a description of R functions and packages that allow their implementat

Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approac

A common set of mathematical tools underlies dynamic optimization, dynamic estimation, and filtering. In Recursive Models of Dynamic Linear Economies, Lars Peter Hansen and Thomas Sargent use these to

Written in a clear, logical and concise manner, this comprehensive resource allows students to quickly understand the key principles, techniques and applications of ordinary differential equations. Important topics including first and second order linear equations, initial value problems and qualitative theory are presented in separate chapters. The concepts of two point boundary value problems, physical models and first order partial differential equations are discussed in detail. The text uses tools of calculus and real analysis to get solutions in explicit form. While discussing first order linear systems, linear algebra techniques are used. The real-life applications are interspersed throughout the book to invoke reader's interest. The methods and tricks to solve numerous mathematical problems with sufficient derivations and explanation are provided. The proofs of theorems are explained for the benefit of the readers.

The purpose of this book is to bring a broad spectrum of readers’ knowledge of matrix algebra that is useful in the statistical analysis of data and in statistics generally. The material is presented

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of inte

The second edition of this popular book provides a thorough grounding in linear, generalized linear and mixed models before providing a framework for GAMs. Although the material is technical, the auth

Faraway surveys statistical techniques that grow from a linear model, describing three extensions. A practicing statistician needs to be aware of and familiar with the basic use of a broad range of id

In nonparametric and high-dimensional statistical models, the classical Gauss-Fisher-Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, on approximation and wavelet theory, and on the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is then presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In the final chapter, the theory of adaptive inference in nonparametric models is deve

Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples

Features step-by-step examples based on actual data and connects fundamental mathematical modeling skills and decision making concepts to everyday applicabilityFeaturing key linear programming, matrix

This edited volume highlights the scientific contributions of Volker Mehrmann, a leading expert in the area of numerical (linear) algebra, matrix theory, differential-algebraic equations and control t

This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated S

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of inte

Directly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models

A common set of mathematical tools underlies dynamic optimization, dynamic estimation, and filtering. In Recursive Models of Dynamic Linear Economies, Lars Peter Hansen and Thomas Sargent use these to