Statistical Inference

108課綱閱讀聽力素養培植書！ 以名著閱讀法及聽重點的聽力訓練策略 用英文故事打造聽讀的素養力！ 閱讀英文故事能培養108課綱強調的閱讀理解能力，在讀故事書時，會反覆演練「理解內容、統整前後文推理、反思評價提出概念」的過程。讀懂故事能深化閱讀素養力，也可以培養更寬廣的思辨分析力與想像力，更能鍛鍊學習腦！ 透過本書精心設計的閱讀聽力學習策略，循序漸進地掌握閱讀故事的重點，實戰練習累積閱讀素養。全書精選8篇格林童話經典故事，以全英文的課本（Main Book）與聽力訓練書（Training Book）兩大部分，堅實打造直接閱讀、聽懂原文名著的實力。了解經典童話，就能品嚐西方文學的魔幻風采。 原文為Margaret Hunt英譯版本，因所使用字彙及用法較難，本書特以全民英檢中級程度的字彙及文法加以改寫，根據童話故事所使用的字彙與字數，將故事由易至難分兩個學習階段（2 Steps），帶領讀者由淺入深漸進學習。 本書特色 1. 彩繪插圖逗趣吸睛，Fun學經典文學 嚴選8篇格林童話經典故事，搭配手繪質感的彩繪插圖，讓學習變得有趣活潑。故事內容包含大拇哥、小紅帽、金鵝、糖果屋、壞矮人、青蛙王子、睡美人、灰姑娘，體驗原汁原味的經典童話，發現故事人物鮮為人知的個性……。 2. 全英文讀本，不查字典也能懂 • 精選文中不易理解的英文單字搭配彩圖，圖像學習更好記。 • 關鍵字彙與片語英英解釋，搭配上下文，情境中熟悉字彙與片語效果最佳。 • 行間穿插英文注釋，培養以英文理解英文的閱讀能力，輕鬆讀懂原文書，閱讀能力大倍增。 3. 聽力訓練書，關鍵字彙複習，英語聽力 Up! Up! • 以配合題複習字彙，再以聽力填空題引導學生聽關鍵字或片語，聽解原文，同時強化記憶單字發音，提升整體聽力能力。 • 字彙複習，加深單字記憶度、再次溫習使用情境。 • 引導聽重點字彙，有效聽力策略教你擺脫「字字都要聽懂」的迷思，聽懂原文名著非難事。 4. 多元測驗即時檢驗學習成果 引導式問題（Stop & Think） 訓練你抓出文章細節（details）、推論文章涵義（make inference），以及培養獨立思考的能力。 英語檢定常見題型（Check Up）選擇題、字彙選填、是非題及配合題，為參加考試作準備。 5. 生動故事朗讀MP3 由專業母語人士以正確、清晰的發音朗讀精采故事，引人入勝，更能加強

過去50年經營管理的反思——如何轉危為安？ 品管大師戴明的傳世經典經營管理的百科全書變革必須從領導者做起，由上往下帶動，才有可能成功。 本書是品管大師戴明（W. Edwards Deming）留給世人的寶藏——關於品質、系統觀、經營管理的知識體系，還有，只用數字來管理員工將會帶來的禍害。 戴明的專長是統計品質管制（SQC，statistical quality control），身為統計學者，他重視「追本溯源」找出真正的問題，而不是「順流而下」只解決表象問題。他透過紅珠實驗（red bead experiment）和漏斗實驗（funnel experiment）提醒管理者，必須注意外力的介入與過度干預所造成的變異（variation）。管理者必須區分變異的發生是出於「共同原因」（common cause）或「特殊原因」（special cause），才能有助於減少變異。戴明認為「變異愈小，品質愈好」，一套能讓員工覺得受到肯定的制度，遠勝於以產量或數字衡量員工的表現，品質會從這個時間點開始，愈來愈好。 戴明提醒管理者必須熟知系統與變異理論，才能了解「只要超乎系統能力的一切作為，都只是徒勞無功」，以免過度迷信表面的數字，或是將未達數字目標的罪過歸咎於員工，卻忽略了潛在的危機。他再三強調，管理者有責任了解問題是出自「系統」還是「個人」；根據研究，一切作業上的問題，85%出自系統內部，而且應該由最高管理者承擔責任，只有15%的問題可歸咎於員工。 雖然戴明從統計的專業，認為凡事都應以數字為證據，不可過度依賴經驗或直覺，不過，他也提醒管理者，最重要的事情往往是無法測量的，設定配額、一日工作量與論件計酬等等的數字標準，可能會帶來禍害。根據他的研究，凡事只講求追求數字的配額制度，有八成的結果會導致效率降低、成本增加，甚至有些人不計手段以達成業績，不但傷害品質也造成公司聲譽受損。最重要的數字往往是那些看不見、無法取得的數字，像是來自滿意顧客的綜效、或是不滿意顧客造成的負面影響，就無法以數字估算。也因此，戴明很反對所謂的目標管理（MBO），反對設定目標然後根據達成率給予獎勵。他認為，按件計酬是人類最退化的手段，看似鼓勵人們增產，卻忽略了品質低落的殺傷力。 戴明說：「必須有勇氣，才能承認自己一直都做錯了某些事，承認自己有待學習，還有更好的方法。」當

Who would have guessed that four minutes could change everything?Today should be one of the worst days of seventeen-year-old Hadley Sullivan's life. Having missed her flight, she's stuck at JFK airpor

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

An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis.This book offers an introduction to the Bayesian approa

This book is a readable, digestible introduction to exponential families, encompassing statistical models based on the most useful distributions in statistical theory, including the normal, gamma, binomial, Poisson, and negative binomial. Strongly motivated by applications, it presents the essential theory and then demonstrates the theory's practical potential by connecting it with developments in areas like item response analysis, social network models, conditional independence and latent variable structures, and point process models. Extensions to incomplete data models and generalized linear models are also included. In addition, the author gives a concise account of the philosophy of Per Martin-Löf in order to connect statistical modelling with ideas in statistical physics, including Boltzmann's law. Written for graduate students and researchers with a background in basic statistical inference, the book includes a vast set of examples demonstrating models for applications and exerc

This book is a readable, digestible introduction to exponential families, encompassing statistical models based on the most useful distributions in statistical theory, including the normal, gamma, binomial, Poisson, and negative binomial. Strongly motivated by applications, it presents the essential theory and then demonstrates the theory's practical potential by connecting it with developments in areas like item response analysis, social network models, conditional independence and latent variable structures, and point process models. Extensions to incomplete data models and generalized linear models are also included. In addition, the author gives a concise account of the philosophy of Per Martin-Löf in order to connect statistical modelling with ideas in statistical physics, including Boltzmann's law. Written for graduate students and researchers with a background in basic statistical inference, the book includes a vast set of examples demonstrating models for applications and exerc

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, chang

This book explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations, in partic

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated

This monograph contributes to the area of comparative statistical inference. Attention is restricted to the important subfield of statistical estimation. The book is intended for an audience having a

Exact statistical inference may be employed in diverse fields of science and technology. As problems become more complex and sample sizes become larger, mathematical and computational difficulties can arise that require the use of approximate statistical methods. Such methods are justified by asymptotic arguments but are still based on the concepts and principles that underlie exact statistical inference. With this in perspective, this book presents a broad view of exact statistical inference and the development of asymptotic statistical inference, providing a justification for the use of asymptotic methods for large samples. Methodological results are developed on a concrete and yet rigorous mathematical level and are applied to a variety of problems that include categorical data, regression, and survival analyses. This book is designed as a textbook for advanced undergraduate or beginning graduate students in statistics, biostatistics, or applied statistics but may also be used as a

This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers mo

Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. For example, it is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. Thisbook seeks to provide a clear exposition of these approaches within a unified framework. The principal market for the book will be students and professionals in philosophy, computer science, and AI. Among the special features of the book are a chapter on evidential probability, which has not received a basic exposition before; chapters on nonmonotonic reasoning and theory replacement, matters rarely addressed in standard philosophical texts; and chapters on Mill's methods and statistical inference that cover material sorely lacking in the usual treatments of AI and computer science.

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance – simulation and sampling, as well as experimental design and data collection – that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance – simulation and sampling, as well as experimental design and data collection – that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior―a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for un

Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as

This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of ef

Includes questions and answers on Exploratory Analysis, Collecting and Producing Data, Probability, and Statistical Inference. Don't just learn why your answer is correct?learn the rationale behind wh

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available

Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It c

Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model se

The parameter estimation and hypothesis testing are the basic tools in statistical inference. These technics occur in many applications of data processing. Besides that, methods of Monte Carlo type ha

This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. I

This textbook provides a coherent introduction to the main concepts and methods of one-parameter statistical inference. Intended for students of Mathematics taking their first course in Statistics, th

In a detailed, coherent treatment of the practices of measurement modeling from a Bayesian perspective, this book covers foundational concepts of Bayesian inference and statistical modeling and their

This book delves into the authors’ work toward deeper understanding of statistical inference in terms of reasoning with uncertainty and meaningfulness of probabilistic inferential output. Focusing on

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with spe

Taking a broad approach, this book explores recent developments in statistics that have impacted actuarial science. It covers inference, Bayesian methods, statistical learning, spatial analysis, and e

Questions and answers on this set of more than 450 flash cards encompass four general statistics-based themes: exploratory analysis, planning a study, probability, and statistical inference. New to th

This book presents a coarse-to-fine framework for learning and inference in large statistical models for natural language processing. The text shows applications of this fast, accurate approach to syn

This introduction to the world of statistics covers exploratory data analysis, methods for collecting data, formal statistical inference, and techniques of regression and analysis of variance. 1983 ed

The Framework for Post-Phylogenetic Systematics reframes biological systematics to reconcile classical and cladistic schools. It combines scientific intuition and statistical inference in a new form o

Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorou

Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of nondetections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it invaluable for graduate students and researchers facing complex data analysis tasks. A link to the author's website for this book ca

The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discov

The theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by

Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes

Recent Advances in System Reliability discusses developments in modern reliability theory such as signatures, multi-state systems and statistical inference. It describes the latest achievements in the