Bayesian Networks in R
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 l...
Bayesian Networks
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data s...
Bayesian Networks
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data s...
Bayesian Networks
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, pred...
Bayesian Networks
Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks, networks with heterogeneous variables, and model validation. The first three chapters explain the whole process of Bayesian network modelling, from structure learning to parameter learning to inference. These chapters cover discrete, Gaussian, and conditional Gaussian Bayesian networks. The following two chapters delve into dynamic networks (to model temporal data) and into networks including arbitrary random variables (using Stan). The book then gives a concise but rigorous treatment of the fundamentals of
Benefits of Bayesian Network Models
The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A large number of scientific publications show the interest in the applications of BN in this field. Unfortuna...
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a c...
Bayesian Learning for Neural Networks
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book dem...
Network Processors
Network processors are the basic building blocks of today's high-speed, high-demand, quality-oriented communication networks. Designing and implementing network processors requires a new programming paradigm and an in-depth understanding of networ...
Network+ Guide to Networks
Master the technical skills and industry knowledge you need to begin an exciting career installing, configuring and troubleshooting computer networks with West/Dean/Andrews' NETWORK+ GUIDE TO NETWORKS, 8th edition. It thoroughly prepares you for s...
Bayesian Computation with R
There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumbe...
Bayesian Computation with R
There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumbe...
Bayesian Essentials with R
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provide...
Modeling and Reasoning with Bayesian Networks
This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques...
Modeling and Reasoning with Bayesian Networks
This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques...
Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science
Bayesian Networks "This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation." Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Bayesian Networks for Proba...
Machine Learning
Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines. Bayesian learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are
Bayesian Analysis of Infectious Diseases
Bayesian Analysis of Infectious Diseases -COVID-19 and Beyond shows how the Bayesian approach can be used to analyze the evolutionary behavior of infectious diseases, including the coronavirus pandemic. The book describes the foundation of Bayesia...
CompTIA Network+ Guide to Networks
Master the technical skills and industry knowledge you need to begin an exciting career installing, configuring and troubleshooting computer networks with West's completely updated NETWORK+ GUIDE TO NETWORKS, 9E. This resource thoroughly prepares ...
Isospectral Transformations
This book presents a new approach to the analysis of networks, which emphasizes how one can compress a network while preserving all information relative to the network's spectrum. Besides these compression techniques, the authors introduce a numbe...
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encourag...
Business in Networks
This seminal book, based on 30 years of research, provides a radical insight into the reality of the business landscape and the operations and management of companies within it. The book challenges orthodox ideas of the business market and busines...
Networks in Contention
How do civil society organizations mobilize on climate change? Why do they choose certain strategies over others? What are the consequences of these choices? Networks in Contention examines how the interactions between different organizations with...
Culture in Networks
Today, interest in networks is growing by leaps and bounds, in both scientific discourse and popular culture. Networks are thought to be everywhere - from the architecture of our brains to global transportation systems. And networks are especially...
Culture in Networks
Today, interest in networks is growing by leaps and bounds, in both scientific discourse and popular culture. Networks are thought to be everywhere from the architecture of our brains to global transportation systems. And networks are especially u...
Living in Networks
How do personal networks emerge from social contexts? How do these evolve during the course of a lifetime? How are relationships established, maintained, connected, disrupted? How does the structure of a network evolve as people face transitions a...
Culture in Networks
Today, interest in networks is growing by leaps and bounds, in both scientific discourse and popular culture. Networks are thought to be everywhere - from the architecture of our brains to global transportation systems. And networks are especially...
Management in Networks
Getting what you want - even if you are the boss - isn't always easy. Almost every organization, big or small, works among a network of competing interests. Whether it's governments pushing through policies, companies trying to increase profits, o...
Accounting in Networks
Accounting in Networks is the first book that in a comprehensive way covers the emerging issue of accounting and control in horizontal relations across legally independent organizations. During the last 20 years, organisations have shown an increa...
Accounting in Networks
Accounting in Networks is the first book that in a comprehensive way covers the emerging issue of accounting and control in horizontal relations across legally independent organizations. During the last 20 years, organisations have shown an increa...
Bayesian Thinking in Biostatistics
Praise for Bayesian Thinking in Biostatistics: "This thoroughly modern Bayesian book ?is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodol...
Bayesian Optimization in Action
Apply advanced techniques for optimising machine learning processes For machine learning practitioners confident in maths and statistics. Bayesian Optimization in Action shows you how to optimise hyperparameter tuning, A/B testing, and othe...
Bayesian Methods in Finance
Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial m...
Statistical Rethinking
Winner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA) Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Refle...
Designing Green Networks and Network Operations
In recent years, socio-political trends toward environmental responsibility and the pressing need to reduce Run-the-Engine (RTE) costs have resulted in the concept of Green IT. Although a significant amount of energy is used to operate routing, sw...
Risk Assessment and Decision Analysis with Bayesian Networks
Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information...
User's Guide to Network Analysis in R
Presenting a comprehensive resource for the mastery of network analysis in R,¿the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of n...
User's Guide to Network Analysis in R
Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundat...