Complex Data Modeling and Computationally Intensive Statistical Methods

★ 從達文西到當代建築，揭開「互承結構」千年不墜的設計奧祕★ 結構力學 × 幾何美感 × 空間創意，一本看懂建築中最迷人的支撐藝術★ 收錄英美日精彩案例，帶你看見理性與詩意並存的建築風景所謂 reciprocal frame，指的是由彼此支撐的梁材所組成的三維結構。看似簡單，背後卻蘊藏驚人的幾何秩序與空間美感。本書從中世紀建築草圖、達文西與Serlio的歷史設計出發，循序帶領讀者理解互承結構的形成原理、形態變化，以及在現代建築中的實際應用。書中不僅解析設計、施工與結構層面的關鍵思考，也透過英國、美國、日本等地的精彩案例，呈現這類建築如何在輕盈、優雅與穩固之間取得動人平衡。從低環境衝擊的自力建造，到日本極具美感的劇場、住宅與博物館，本書讓古老智慧重新進入當代建築語言，為建築師、結構工程師，以及所有熱愛空間設計的人，打開一扇全新的觀看之門。In structural terms reciprocal frame structures are 'three dimensional assemblies of mutually supporting beams'. But behind this definition lie some breathtakingly beautiful and complex structures at the heart of buildings both ancient and modern. This new book explores the principles of these apparently simple structures and demonstrates how they can be used in the context of a modern building.Starting with historic designs by de Honnecourt, Da Vinci and Serlio, the book presents the wealth of possible RF morphologies, and investigates the geometrical, structural and practical design issues of reciprocal frames. The ca

This busy family needs a vacation, but they don?t know where to go. Mom and Dad want peace and quiet. Grandma wants to go somewhere hot. Fluffer wants to go somewhere that pets can go, too! The pig-ta

This book provides an in-depth roadmap for qualitative research methods and data analysis across disciplines using ATLAS.ti. It encompasses and rationalizes key qualitative research concepts and methods with innovative frameworks to enrich the reader’s understanding of qualitative research methods and data analysis. The book contains multiple real-world projects that assist readers in developing conceptual and theoretical knowledge, as well as gaining insights from fieldwork, visualizing data collection, analysis, and methodological interpretations.This easy to use book provides a pragmatic approach to qualitative methods, proficiency in capturing field observations, user-friendly steps to data analysis starting from transcription, preparation of data, reading and identification of quotations, coding, memoing, commenting, code groups, creating smart codes and groups using set, semantic, and proximity operators resulting in generating numerous insightful thematic reports in avariety of

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.

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets.Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow―and show how a human-centered approach can enhance each one, by making the process more transparent, aski

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This book focuses on statistical methods which impinge more or less directly on the decisions that are made during the course of pharmaceutical and agro-chemical research, considering the four decisio

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The Concept and Object Modeling Notation (COMN) is able to cover the full spectrum of analysis and design. A single COMN model can represent the objects and concepts in the problem space, logical data

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Learn the value of conceptual, logical, and physical data modeling and how each stage increases our knowledge of the data and reduces assumptions and poor design decisions in MongoDB.

"Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural netwo

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