Gated Bayesian Networks

Gated Bayesian Networks PDF

Author: Marcus Bendtsen

Publisher: Linköping University Electronic Press

Published: 2017-06-08

Total Pages: 213

ISBN-13: 9176855252

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Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. The random variables and the relationships among them decide the structure of the directed acyclic graph that represents the Bayesian network. It is the stasis over time of these two components that we question in this thesis. By introducing a new type of probabilistic graphical model, which we call gated Bayesian networks, we allow for the variables that we include in our model, and the relationships among them, to change overtime. We introduce algorithms that can learn gated Bayesian networks that use different variables at different times, required due to the process which we are modelling going through distinct phases. We evaluate the efficacy of these algorithms within the domain of algorithmic trading, showing how the learnt gated Bayesian networks can improve upon a passive approach to trading. We also introduce algorithms that detect changes in the relationships among the random variables, allowing us to create a model that consists of several Bayesian networks, thereby revealing changes and the structure by which these changes occur. The resulting models can be used to detect the currently most appropriate Bayesian network, and we show their use in real-world examples from both the domain of sports analytics and finance.

Bayesian Networks

Bayesian Networks PDF

Author: Olivier Pourret

Publisher: John Wiley & Sons

Published: 2008-04-30

Total Pages: 446

ISBN-13: 9780470994542

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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.

Learning Bayesian Networks

Learning Bayesian Networks PDF

Author: Richard E. Neapolitan

Publisher: Prentice Hall

Published: 2004

Total Pages: 704

ISBN-13:

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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.

Bayesian Networks in R

Bayesian Networks in R PDF

Author: Radhakrishnan Nagarajan

Publisher: Springer Science & Business Media

Published: 2014-07-08

Total Pages: 168

ISBN-13: 1461464463

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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.

Bayesian Network Technologies: Applications and Graphical Models

Bayesian Network Technologies: Applications and Graphical Models PDF

Author: Mittal, Ankush

Publisher: IGI Global

Published: 2007-03-31

Total Pages: 368

ISBN-13: 159904143X

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"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.

Twelfth Scandinavian Conference on Artificial Intelligence

Twelfth Scandinavian Conference on Artificial Intelligence PDF

Author: M. Jaeger

Publisher: IOS Press

Published: 2013-11-14

Total Pages: 324

ISBN-13: 1614993300

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Artificial intelligence has become so much a part of everyday life that it is now hard to imagine a world without it. This book presents papers from the 12th Scandinavian Conference on Artificial Intelligence (SCAI), held in Aalborg, Denmark in November 2013. The SCAI conference is the main biennial platform for the AI research community in Scandinavia, and the papers collected here not only include contributions from Scandinavia, but also from other European and non-European countries. Topics cover the entire range of AI, with a particular focus on machine learning and knowledge representation, as well as uncertainty in AI and applications. In addition to the 28 regular papers, extended abstracts of the presentations made by Ph.D. students of their research-in-progress to a panel of experts in the doctoral symposium – a new feature at this conference – are also included here. This book will be of interest to all those who wish to keep up-to-date with the latest developments in artificial intelligence.

Probabilistic Methods for Bioinformatics

Probabilistic Methods for Bioinformatics PDF

Author: Richard E. Neapolitan

Publisher: Morgan Kaufmann

Published: 2009-06-12

Total Pages: 421

ISBN-13: 0080919367

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The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics. Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis. Unique coverage of probabilistic reasoning methods applied to bioinformatics data--those methods that are likely to become the standard analysis tools for bioinformatics. Shares insights about when and why probabilistic methods can and cannot be used effectively; Complete review of Bayesian networks and probabilistic methods with a practical approach.

Bayesian Networks for Reliability Engineering

Bayesian Networks for Reliability Engineering PDF

Author: Baoping Cai

Publisher: Springer

Published: 2019-02-28

Total Pages: 257

ISBN-13: 9811365164

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This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field of reliability. After focusing on the engineering systems, the book subsequently discusses twelve important issues in the BN-based reliability methodologies, such as BN structure modeling, BN parameter modeling, BN inference, validation, and verification. As such, it is a valuable resource for researchers and practitioners in the field of reliability engineering.

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