Complex Data Modeling and Computationally Intensive Statistical Methods

Designed for advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II as well as courses on Generalized Linear Models, Longitudinal Data Analysis, Correl

Corpus linguistics continues to be a vibrant methodology applied across highly diverse fields of research in the language sciences. With the current steep rise in corpus sizes, computational power, statistical literacy and multi-purpose software tools, and inspired by neighbouring disciplines, approaches have diversified to an extent that calls for an intensification of the accompanying critical debate. Bringing together a team of leading experts, this book follows a unique design, comparing advanced methods and approaches current in corpus linguistics, to stimulate reflective evaluation and discussion. Each chapter explores the strengths and weaknesses of different datasets and techniques, presenting a case study and allowing readers to gauge methodological options in practice. Contributions also provide suggestions for further reading, and data and analysis scripts are included in an online appendix. This is an important and timely volume, and will be essential reading for any lingui

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

Fay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any problem and enough theory to tackle new problems by modifying existing methods. After covering the basic mathematical theory and scientific principles, tests and confidence intervals are developed for specific types of data. Essential methods for applications are covered, such as general procedures for creating tests (e.g., likelihood ratio, bootstrap, permutation, testing from models), adjustments for multiple testing, clustering, stratification, causality, censoring, missing data, group sequential tests, and non-inferiority tests. New methods developed by the authors are included throughout, such as melded confidence intervals for comparing two samples and confidence intervals associated with Wil

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.

An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and

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

Why did Egyptian cults, especially those dedicated to the goddess Isis and god Sarapis, spread so successfully across the ancient Mediterranean after the death of Alexander the Great? How are we limited by the established methodological apparatus of historiography and which innovative methods from other disciplines can overcome these limits? In this book, Tomáš Glomb shows that while the interplay of different factors such as the economy, climate, and politics created favorable conditions for the early spread of the Isiac cults, the use of innovative quantitative methods can shed new light and help disentangle the complex interplay of individual factors. Using a combination of geospatial modeling, mathematical modeling, and network analysis, Glomb determines that, at least in the regions of the Hellenistic Aegean and western Asia Minor, the political channels created by the Ptolemaic dynasty were a dominant force in the local spread of the Isiac cults. An important contribution to th

A cognitive ethnography of how bioengineering scientists create innovative modeling methods.In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering scientists integrate the cognitive, social, material, and cultural dimensions of practice. Her findings and conclusions have broad implications for researchers in philosophy, science studies, cognitive science, and interdisciplinary studies, as well as scientists, educators, policy makers, and funding agencies. In studying the epistemic practices of scientists, Ner

Authoritative resource exploring the fundamental theories and concepts in the field of personality psychology In Personality, psychodynamic, social-cognitive, phenomenological, and trait-theoretic perspectives are presented in an unbiased--yet critical--fashion that encourages students to compare theories, evaluate evidence, analyze data, and form their own conclusions. Thorough historical coverage of the subject is balanced with discussions of the current state of the field, providing a solid understanding of theory and methods as relevant to practice today. The text provides a comparative learning framework that capitalizes on student intuition to encourage critical thinking and self-exploration and highlights the contributions of neuroscience in advancing all aspects of personality theory and research. Complex ideas are presented in an accessible, easy-to-follow style that directly engages students without oversimplifying critical points. To aid in reader comprehension, the text inc

This book provides a series of gesture and behavior recognition methods based on multimodal datarepresentation. The data modalities include image data and skeleton data, and the modeling methods includetraditional codebook, topological graph, and LSTM architectures. The tasks include single gesture recognitionclassification, single action recognition classification, continuous gesture classification, complex behaviorclassification of human interaction and other tasks of different complexity. This book focuses on the dataprocessing methods of each modality, and the modeling methods for different tasks. We hope the reader canlearn basic gesture and action recognition methods from this book, and develop a model system that suits theirneeds on this basis. This book can be used as a textbook for graduate, postgraduate and PhD students majoring in computerscience, automation, etc. It can also be used as a reference for the reader who is interested in gesturerecognition, human action inter

Learn to assess electromigration reliability and design more resilient chips in this comprehensive and practical resource. Beginning with fundamental physics and building to advanced methodologies, this book enables the reader to develop highly reliable on-chip wiring stacks and power grids. Through a detailed review on the role of microstructure, interfaces and processing on electromigration reliability, as well as characterisation, testing and analysis, the book follows the development of on-chip interconnects from microscale to nanoscale. Practical modeling methodologies for statistical analysis, from simple 1D approximation to complex 3D description, can be used for step-by-step development of reliable on-chip wiring stacks and industrial-grade power/ground grids. This is an ideal resource for materials scientists and reliability and chip design engineers.

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod