Download or read book Bayesian Networks in R written by Radhakrishnan Nagarajan and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.

Download or read book Pulsed Neural Networks written by Wolfgang Maass and published by MIT Press. This book was released on 2001-01-26 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. In recent years, data from neurobiological experiments have made it increasingly clear that biological neural networks, which communicate through pulses, use the timing of the pulses to transmit information and perform computation. This realization has stimulated significant research on pulsed neural networks, including theoretical analyses and model development, neurobiological modeling, and hardware implementation. This book presents the complete spectrum of current research in pulsed neural networks and includes the most important work from many of the key scientists in the field. Terrence J. Sejnowski's foreword, "Neural Pulse Coding," presents an overview of the topic. The first half of the book consists of longer tutorial articles spanning neurobiology, theory, algorithms, and hardware. The second half contains a larger number of shorter research chapters that present more advanced concepts. The contributors use consistent notation and terminology throughout the book. Contributors Peter S. Burge, Stephen R. Deiss, Rodney J. Douglas, John G. Elias, Wulfram Gerstner, Alister Hamilton, David Horn, Axel Jahnke, Richard Kempter, Wolfgang Maass, Alessandro Mortara, Alan F. Murray, David P. M. Northmore, Irit Opher, Kostas A. Papathanasiou, Michael Recce, Barry J. P. Rising, Ulrich Roth, Tim Schönauer, Terrence J. Sejnowski, John Shawe-Taylor, Max R. van Daalen, J. Leo van Hemmen, Philippe Venier, Hermann Wagner, Adrian M. Whatley, Anthony M. Zador

Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1740 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.

Download or read book Learning Bayesian Networks written by Richard E. Neapolitan and published by Prentice Hall. This book was released on 2004 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.

Download or read book Probabilistic Methods for Financial and Marketing Informatics written by Richard E. Neapolitan and published by Elsevier. This book was released on 2010-07-26 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning to business problems. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors concentrate on showing examples and using the software package Netica to represent and solve problems. The book contains unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. This book is recommended for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes computer science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used are in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science. - Unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance - Shares insights about when and why probabilistic methods can and cannot be used effectively - Complete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics.

Download or read book Bayesian Networks written by Marco Scutari and published by CRC Press. This book was released on 2021-07-28 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the material step-by-step starting from meaningful examples Steps detailed with R code in the spirit of reproducible research Real world data analyses from a Science paper reproduced and explained in detail Examples span a variety of fields across social and life sciences Overview of available software in and outside R

Download or read book Introduction to Bayesian Networks written by Finn V. Jensen and published by Springer. This book was released on 1997-08-15 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disk contains: Tool for building Bayesian networks -- Library of examples -- Library of proposed solutions to some exercises.

Download or read book Bayesian Network Technologies Applications and Graphical Models written by Mittal, Ankush and published by IGI Global. This book was released on 2007-03-31 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides an excellent, well-balanced collection of areas where Bayesian networks have been successfully applied; it describes the underlying concepts of Bayesian Networks with the help of diverse applications, and theories that prove Bayesian networks valid"--Provided by publisher.

Download or read book Bayesian Networks written by Olivier Pourret and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.

Download or read book Bayesian Inference for Gene Expression and Proteomics written by Kim-Anh Do and published by Cambridge University Press. This book was released on 2006-07-24 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.

Download or read book Bayesian Network written by Ahmed Rebai and published by BoD – Books on Demand. This book was released on 2010-08-18 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and perception. They provide a language that supports efficient algorithms for the automatic construction of expert systems in several different contexts. The range of applications of Bayesian networks currently extends over almost all fields including engineering, biology and medicine, information and communication technologies and finance. This book is a collection of original contributions to the methodology and applications of Bayesian networks. It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the modeling of complex systems. Readers that are not familiar with this tool, but have some technical background, will find in this book all necessary theoretical and practical information on how to use and implement Bayesian networks in their own work. There is no doubt that this book constitutes a valuable resource for engineers, researchers, students and all those who are interested in discovering and experiencing the potential of this major tool of the century.

Download or read book Data Analysis and Visualization in Genomics and Proteomics written by Francisco Azuaje and published by John Wiley & Sons. This book was released on 2005-06-24 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems. One of the first systematic overviews of the problem of biological data integration using computational approaches This book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scale Places emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems

Download or read book Bioinformatics Using Computational Intelligence Paradigms written by Udo Seiffert and published by Springer Science & Business Media. This book was released on 2005-01-17 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bioinformatics and computational intelligence are undoubtedly remarkably fast growing fields of research and real-world applications with enormous potential for current and future developments. Bioinformatics Using Computational Intelligence Paradigms contains recent theoretical approaches and guiding applications of biologically inspired information processing systems (computational intelligence) against the background of bioinformatics. This carefully edited monograph combines the latest results of bioinformatics and computational intelligence, and offers promising cross-fertilization and interdisciplinary work between these growing fields.

Download or read book Gene Network Inference written by Alberto Fuente and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians.

Download or read book Bioinformatics for Systems Biology written by Stephen Krawetz and published by Springer Science & Business Media. This book was released on 2008-12-11 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bioinformatics for Systems Biology bridges and unifies many disciplines. It presents the life scientist, computational biologist, and mathematician with a common framework. Only by linking the groups together may the true life sciences revolution move forward.

Download or read book Probabilistic Modeling in Bioinformatics and Medical Informatics written by Dirk Husmeier and published by Springer Science & Business Media. This book was released on 2006-05-06 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.