Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Models And Interpretations
Download Models And Interpretations full books in PDF, epub, and Kindle. Read online Models And Interpretations ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Book Synopsis Efforts and Models in Interpreting and Translation Research by : Gyde Hansen
Download or read book Efforts and Models in Interpreting and Translation Research written by Gyde Hansen and published by John Benjamins Publishing. This book was released on 2009-01-05 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume covers a wide range of topics in Interpreting and Translation Research. Some deal with scientometrics and the history of Interpreting Studies, arguments about conceptual analysis, meta-language and interpreters’ risk-taking strategies. Other papers are on research skills like career management, writing communicative abstracts and the practicalities of survey research. Several contributions address empirical issues such as expertise in Simultaneous Interpreting, the cognitive load imposed on interpreters by a non-native accent, the impact of intonation on interpreting quality, linguistic interference in Simultaneous Interpreting, similarities between translation and interpreting, and the relation between translation competence and revision competence. The collection is a tribute to Daniel Gile, in appreciation of his creativity and his commitment to interpreting and translation research. All the contributions in some way show his influence or are related to the models and research he has shaped.
Book Synopsis Empirical modelling of translation and interpreting by : Hansen-Schirra, Silvia
Download or read book Empirical modelling of translation and interpreting written by Hansen-Schirra, Silvia and published by Language Science Press. This book was released on 2017 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empirical research is carried out in a cyclic way: approaching a research area bottom-up, data lead to interpretations and ideally to the abstraction of laws, on the basis of which a theory can be derived. Deductive research is based on a theory, on the basis of which hypotheses can be formulated and tested against the background of empirical data. Looking at the state-of-the-art in translation studies, either theories as well as models are designed or empirical data are collected and interpreted. However, the final step is still lacking: so far, empirical data has not lead to the formulation of theories or models, whereas existing theories and models have not yet been comprehensively tested with empirical methods. This publication addresses these issues from several perspectives: multi-method product- as well as process-based research may gain insights into translation as well as interpreting phenomena. These phenomena may include cognitive and organizational processes, procedures and strategies, competence and performance, translation properties and universals, etc. Empirical findings about the deeper structures of translation and interpreting will reduce the gap between translation and interpreting practice and model and theory building. Furthermore, the availability of more large-scale empirical testing triggers the development of models and theories concerning translation and interpreting phenomena and behavior based on quantifiable, replicable and transparent data.
Download or read book Forall X written by P. D. Magnus and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Models in Software Engineering by : Michel R. V. Chaudron
Download or read book Models in Software Engineering written by Michel R. V. Chaudron and published by Springer. This book was released on 2009-04-28 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes a collection of the best papers selected from the 12 workshops and 3 tutorials held in conjunction with MODELS 2008, the 11th International Conference on Model Driven Engineering Languages and Systems, in Toulouse, France, September 28 - October 3, 2008. The contributions are organized within the volume according to the workshops at which they were presented: Model Based Architecting and Construction of Embedded Systems (ACES-MB); Challenges in Model Driven Software Engineering (CHAMDE); Empirical Studies of Model Driven Engineering (ESMDA); Models@runtime; Model Co-evolution and Consistency Management (MCCM); Model-Driven Web Engineering (MDWE); Modeling Security (MODSEC); Model-Based Design of Trustworthy Health Information Systems (MOTHIS); Non-functional System Properties in Domain Specific Modeling Languages (NFPin DSML); OCL Tools: From Implementation to Evaluation and Comparison (OCL); Quality in Modeling (QIM); and Transforming and Weaving Ontologies and Model Driven Engineering (TWOMDE). Each section includes a summary of the workshop. The last three sections contain selected papers from the Doctoral Symposium, the Educational Symposium and the Research Project Symposium, respectively.
Book Synopsis Ecological Models and Data in R by : Benjamin M. Bolker
Download or read book Ecological Models and Data in R written by Benjamin M. Bolker and published by Princeton University Press. This book was released on 2008-07-21 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.
Book Synopsis Conceptual Design by : Mogens Myrup Andreasen
Download or read book Conceptual Design written by Mogens Myrup Andreasen and published by Springer. This book was released on 2015-07-03 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Maximising reader insights into the theory, models, methods and fundamental reasoning of design, this book addresses design activities in industrial settings, as well as the actors involved. This approach offers readers a new understanding of design activities and related functions, properties and dispositions. Presenting a ‘design mindset’ that seeks to empower students, researchers, and practitioners alike, it features a strong focus on how designers create new concepts to be developed into products, and how they generate new business and satisfy human needs. Employing a multi-faceted perspective, the book supplies the reader with a comprehensive worldview of design in the form of a proposed model that will empower their activities as student, researcher or practitioner. We draw the reader into the core role of design conceptualisation for society, for the development of industry, for users and buyers of products, and for citizens in relation to public systems. The book also features original contributions related to exploration, conceptualisation and product synthesis. Exploring both the power and limitations of formal design process models, methods, and tools viewed in the light of human ingenuity and cognition, the book develops a unique design mindset that adds human understanding to the list of methods and tools essential to design. This insight is distilled into useful mindset heuristics included throughout the book.
Book Synopsis Data Analysis Using Regression and Multilevel/Hierarchical Models by : Andrew Gelman
Download or read book Data Analysis Using Regression and Multilevel/Hierarchical Models written by Andrew Gelman and published by Cambridge University Press. This book was released on 2007 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu
Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
Book Synopsis Justified Modeling Frameworks and Novel Interpretations of Ecological and Epidemiological Systems by : Bapan Ghosh
Download or read book Justified Modeling Frameworks and Novel Interpretations of Ecological and Epidemiological Systems written by Bapan Ghosh and published by Frontiers Media SA. This book was released on 2024-01-12 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Lotka-Volterra and the Kermack-McKendrick models are well celebrated and widely recognized in the field of ecology and epidemiology. Several modified ordinary differential equation models have been proposed over the last many decades to rationalize complex biological phenomena. In the current century, researchers have paid much attention to developing new modeling frameworks with delay differential equations, difference equations, fractional order systems, stochastic differential equations, etc. No doubt, these models have emerged many new bifurcations theory and methods which have equally contributed to the advances of Mathematics and interdisciplinary research. It is argued that these new modeling frameworks perform more effectively in analyzing and interpreting results compared to the conventional modeling frameworks with ordinary differential equations. However, implications of emerged bifurcations from new modeling approaches are often less interpreted from a biological viewpoint. Even, there is also a lack of understanding of how a fractional order model, for instance, displays a more realistic scenario to analyze a biological process. Therefore, a more serious justification is essential while modeling any biological event.
Book Synopsis Mathematical Modeling by : Sandip Banerjee
Download or read book Mathematical Modeling written by Sandip Banerjee and published by CRC Press. This book was released on 2021-11-11 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Modeling: Models, Analysis and Applications, Second Edition introduces models of both discrete and continuous systems. This book is aimed at newcomers who desires to learn mathematical modeling, especially students taking a first course in the subject. Beginning with the step-by-step guidance of model formulation, this book equips the reader about modeling with difference equations (discrete models), ODE’s, PDE’s, delay and stochastic differential equations (continuous models). This book provides interdisciplinary and integrative overview of mathematical modeling, making it a complete textbook for a wide audience. A unique feature of the book is the breadth of coverage of different examples on mathematical modelling, which include population models, economic models, arms race models, combat models, learning model, alcohol dynamics model, carbon dating, drug distribution models, mechanical oscillation models, epidemic models, tumor models, traffic flow models, crime flow models, spatial models, football team performance model, breathing model, two neuron system model, zombie model and model on love affairs. Common themes such as equilibrium points, stability, phase plane analysis, bifurcations, limit cycles, period doubling and chaos run through several chapters and their interpretations in the context of the model have been highlighted. In chapter 3, a section on estimation of system parameters with real life data for model validation has also been discussed. Features Covers discrete, continuous, spatial, delayed and stochastic models. Over 250 illustrations, 300 examples and exercises with complete solutions. Incorporates MATHEMATICA® and MATLAB®, each chapter contains Mathematica and Matlab codes used to display numerical results (available at CRC website). Separate sections for Projects. Several exercise problems can also be used for projects. Presents real life examples of discrete and continuous scenarios. The book is ideal for an introductory course for undergraduate and graduate students, engineers, applied mathematicians and researchers working in various areas of natural and applied sciences.
Book Synopsis Linear Models in Statistics by : Alvin C. Rencher
Download or read book Linear Models in Statistics written by Alvin C. Rencher and published by John Wiley & Sons. This book was released on 2008-01-07 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
Book Synopsis Business Risk Management by : Edward J. Anderson
Download or read book Business Risk Management written by Edward J. Anderson and published by John Wiley & Sons. This book was released on 2013-10-23 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and accessible introduction to modern quantitative risk management. The business world is rife with risk and uncertainty, and risk management is a vitally important topic for managers. The best way to achieve a clear understanding of risk is to use quantitative tools and probability models. Written for students, this book has a quantitative emphasis but is accessible to those without a strong mathematical background. Business Risk Management: Models and Analysis Discusses novel modern approaches to risk management Introduces advanced topics in an accessible manner Includes motivating worked examples and exercises (including selected solutions) Is written with the student in mind, and does not assume advanced mathematics Is suitable for self-study by the manager who wishes to better understand this important field. Aimed at postgraduate students, this book is also suitable for senior undergraduates, MBA students, and all those who have a general interest in business risk.
Book Synopsis Sensitivity Analysis in Practice by : Andrea Saltelli
Download or read book Sensitivity Analysis in Practice written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2004-07-16 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the concepts discussed. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. Contains a large number of references to sources for further reading. Authored by the leading authorities on sensitivity analysis.
Book Synopsis Semantics - Theories by : Claudia Maienborn
Download or read book Semantics - Theories written by Claudia Maienborn and published by Walter de Gruyter GmbH & Co KG. This book was released on 2019-02-19 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in paperback for the first time since its original publication, the material gathered here is perfect for anyone who needs a detailed and accessible introduction to the important semantic theories. Designed for a wide audience, it will be of great value to linguists, cognitive scientists, philosophers, and computer scientists working on natural language. The book covers theories of lexical semantics, cognitively oriented approaches to semantics, compositional theories of sentence semantics, and discourse semantics. This clear, elegant explanation of the key theories in semantics research is essential reading for anyone working in the area.
Book Synopsis Beyond Multiple Linear Regression by : Paul Roback
Download or read book Beyond Multiple Linear Regression written by Paul Roback and published by CRC Press. This book was released on 2021-01-14 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
Book Synopsis Frontiers in Massive Data Analysis by : National Research Council
Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
