Learning and Predicting with Chain Event Graphs
Author | : Guy Freeman |
Publisher | : |
Total Pages | : 0 |
Release | : 2010 |
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Author | : Guy Freeman |
Publisher | : |
Total Pages | : 0 |
Release | : 2010 |
Genre | : |
ISBN | : |
Author | : Rodrigo A. Collazo |
Publisher | : CRC Press |
Total Pages | : 255 |
Release | : 2018-01-29 |
Genre | : Business & Economics |
ISBN | : 1498729614 |
Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model selection and learning techniques. The book extends established technologies used in the study of discrete Bayesian Networks so that they apply in a much more general setting As the first book on Chain Event Graphs, this monograph is expected to become a landmark work on the use of event trees and coloured probability trees in statistics, and to lead to the increased use of such tree models to describe hypotheses about how events might unfold. Features: introduces a new and exciting discrete graphical model based on an event tree focusses on illustrating inferential techniques, making its methodology accessible to a very broad audience and, most importantly, to practitioners illustrated by a wide range of examples, encompassing important present and future applications includes exercises to test comprehension and can easily be used as a course book introduces relevant software packages Rodrigo A. Collazo is a methodological and computational statistician based at the Naval Systems Analysis Centre (CASNAV) in Rio de Janeiro, Brazil. Christiane Görgen is a mathematical statistician at the Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Jim Q. Smith is a professor of statistics at the University of Warwick, UK. He has published widely in the field of statistics, AI, and decision analysis and has written two other books, most recently Bayesian Decision Analysis: Principles and Practice (Cambridge University Press 2010).
Author | : |
Publisher | : |
Total Pages | : 394 |
Release | : 2008 |
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Author | : Johannes Fürnkranz |
Publisher | : Springer |
Total Pages | : 371 |
Release | : 2013-09-30 |
Genre | : Computers |
ISBN | : 3642408974 |
This book constitutes the proceedings of the 16th International Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances in the development and analysis of methods of automatic scientific knowledge discovery, machine learning, intelligent data analysis, and their application to knowledge discovery.
Author | : William L. William L. Hamilton |
Publisher | : Springer Nature |
Total Pages | : 141 |
Release | : 2022-06-01 |
Genre | : Computers |
ISBN | : 3031015886 |
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author | : Xuewen Yu |
Publisher | : |
Total Pages | : 0 |
Release | : 2022 |
Genre | : Bayesian statistical decision theory |
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Author | : Lorna M. Barclay |
Publisher | : |
Total Pages | : |
Release | : 2014 |
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Author | : Jian Chen |
Publisher | : Springer Nature |
Total Pages | : 255 |
Release | : 2022-06-04 |
Genre | : Computers |
ISBN | : 9811936102 |
This book constitutes the refereed proceedings of the 21st International Symposium on Knowledge and Systems Sciences, KSS 2022, held in Beijing, China, in June 2022. The 14 revised full papers and 3 short paper presented were carefully reviewed and selected from 51 submissions. The papers are organized in topical secions on data mining and machine learning; model-based systems engineering; complex systems modeling and knowledge technologies.
Author | : Luis Enrique Sucar |
Publisher | : Springer |
Total Pages | : 267 |
Release | : 2015-06-19 |
Genre | : Computers |
ISBN | : 144716699X |
This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.
Author | : Peter Spirtes |
Publisher | : Springer Science & Business Media |
Total Pages | : 551 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461227488 |
This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.