Download or read book Handbook of Probabilistic Models written by Pijush Samui and published by Butterworth-Heinemann. This book was released on 2019-10-05 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. - Explains the application of advanced probabilistic models encompassing multidisciplinary research - Applies probabilistic modeling to emerging areas in engineering - Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems

Download or read book Handbook of Graphical Models written by Marloes Maathuis and published by CRC Press. This book was released on 2018-11-12 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art. Key features: * Contributions by leading researchers from a range of disciplines * Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications * Balanced coverage of concepts, theory, methods, examples, and applications * Chapters can be read mostly independently, while cross-references highlight connections The handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.

Download or read book Handbook of Probability written by Tamás Rudas and published by SAGE. This book was released on 2008-02-21 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This is a valuable reference guide for readers interested in gaining a basic understanding of probability theory or its applications in problem solving in the other disciplines." —CHOICE Providing cutting-edge perspectives and real-world insights into the greater utility of probability and its applications, the Handbook of Probability offers an equal balance of theory and direct applications in a non-technical, yet comprehensive, format. Editor Tamás Rudas and the internationally-known contributors present the material in a manner so that researchers of various backgrounds can use the reference either as a primer for understanding basic probability theory or as a more advanced research tool for specific projects requiring a deeper understanding. The wide-ranging applications of probability presented make it useful for scholars who need to make interdisciplinary connections in their work. Key Features Contains contributions from the international who's-who of probability across several disciplines Offers an equal balance of theory and applications Explains the most important concepts of probability theory in a non-technical yet comprehensive way Provides in-depth examples of recent applications in the social and behavioral sciences as well as education, business, and law Intended Audience This Handbook makes an ideal library purchase. In addition, this volume should also be of interest to individual scholars in the social and behavioral sciences.

Download or read book Probabilistic Techniques in Exposure Assessment written by Alison C. Cullen and published by Springer Science & Business Media. This book was released on 1999-07-31 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this text, experts provide a complete sourcebook on methods for addressing variability and uncertainty in exposure analysis.

Download or read book Handbook of Mixed Membership Models and Their Applications written by Edoardo M. Airoldi and published by CRC Press. This book was released on 2014-11-06 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporating more than 20 years of the editors' and contributors' statistical work in mixed membership modeling, this handbook shows how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, readers will discover how to characterize complex multivariate data in a range of areas.

Download or read book Probabilistic Models of the Brain written by Rajesh P.N. Rao and published by MIT Press. This book was released on 2002-03-29 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: A survey of probabilistic approaches to modeling and understanding brain function. Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function. This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.

Download or read book Handbook of Model Checking written by Edmund M. Clarke and published by Springer. This book was released on 2018-05-18 with total page 1210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model checking is a computer-assisted method for the analysis of dynamical systems that can be modeled by state-transition systems. Drawing from research traditions in mathematical logic, programming languages, hardware design, and theoretical computer science, model checking is now widely used for the verification of hardware and software in industry. The editors and authors of this handbook are among the world's leading researchers in this domain, and the 32 contributed chapters present a thorough view of the origin, theory, and application of model checking. In particular, the editors classify the advances in this domain and the chapters of the handbook in terms of two recurrent themes that have driven much of the research agenda: the algorithmic challenge, that is, designing model-checking algorithms that scale to real-life problems; and the modeling challenge, that is, extending the formalism beyond Kripke structures and temporal logic. The book will be valuable for researchers and graduate students engaged with the development of formal methods and verification tools.

Download or read book Foundations of Probabilistic Programming written by Gilles Barthe and published by Cambridge University Press. This book was released on 2020-12-03 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.

Download or read book Handbook of Probabilistic Models written by Pijush Samui and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of probabilistic models carefully examines the application of advanced probabilistic models in conventional engineering fields.

Download or read book Handbook of Latent Semantic Analysis written by Thomas K. Landauer and published by Psychology Press. This book was released on 2007-02-15 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Latent Semantic Analysis is the authoritative reference for the theory behind Latent Semantic Analysis (LSA), a burgeoning mathematical method used to analyze how words make meaning, with the desired outcome to program machines to understand human commands via natural language rather than strict programming protocols. The first book

Download or read book A User s Guide to Measure Theoretic Probability written by David Pollard and published by Cambridge University Press. This book was released on 2002 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean.

Download or read book Handbook of Time Series Analysis written by Björn Schelter and published by John Wiley & Sons. This book was released on 2006-12-13 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest developments will profit from this handbook.

Download or read book Handbook of Neural Computation written by Pijush Samui and published by Academic Press. This book was released on 2017-07-18 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. - Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more - Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing - Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

Download or read book The SAGE Handbook of Quantitative Methodology for the Social Sciences written by David Kaplan and published by SAGE. This book was released on 2004-06-21 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative methodology is a highly specialized field, and as with any highly specialized field, working through idiosyncratic language can be very difficult made even more so when concepts are conveyed in the language of mathematics and statistics. The Sage Handbook of Quantitative Methodology for the Social Sciences was conceived as a way of introducing applied statisticians, empirical researchers, and graduate students to the broad array of state-of-the-art quantitative methodologies in the social sciences. The contributing authors of the Handbook were asked to write about their areas of expertise in a way that would convey to the reader the utility of their respective methodologies. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter. The Handbook consists of six sections comprising twenty-five chapters, from topics in scaling and measurement, to advances in statistical modelling methodologies, and finally to broad philosophical themes that transcend many of the quantitative methodologies covered in this handbook.

Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Download or read book Handbook of Statistical Systems Biology written by Michael Stumpf and published by John Wiley & Sons. This book was released on 2011-09-09 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.

Download or read book Bayesian Methods for Hackers written by Cameron Davidson-Pilon and published by Addison-Wesley Professional. This book was released on 2015-09-30 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.