Lists Decisions And Graphs

A practical guide to normative systems: Causal and bayesian networks; Building models; learning, adaptation, and tuning; Decision graphs. Algorithms ofr normative systems: Belief updating in bayesian networks; Bayesian network analysis tools; Algorithms ofr influence diagrams. List of notation.

Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.

This book constitutes the refereed proceedings of the First European Conference on Technology Enhanced Learning, EC-TEL 2006. The book presents 32 revised full papers, 13 revised short papers and 31 poster papers together with 2 keynote talks. Topics addressed include collaborative learning, personalized learning, multimedia content, semantic web, metadata and learning, workplace learning, learning repositories and infrastructures for learning, as well as experience reports, assessment, and case studies, and more.

Models are used in all kinds of engineering disciplines to abstract from the various details of the modelled entity in order to focus on a speci?c aspect. Like a blueprint in civil engineering, a software architecture providesan abstraction from the full software system’s complexity. It allows software designers to get an overview on the system underdevelopmentandtoanalyzeitsproperties.Inthissense,modelsarethefoundation needed for software development to become a true engineering discipline. Especially when reasoning on a software system’s extra-functional properties, its software architecture carries the necessary information for early, design-time analyses. These analyses take the software architecture as input and can be used to direct the design process by allowing a systematic evaluation of different design alternatives. For example, they can be used to cancel out decisions which would lead to architecture - signs whose implementation would not comply with extra-functionalrequirements like performance or reliability constraints. Besides such quality attributes directly visible to the end user, internal quality attributes, e.g., maintainability, also highly depend on the system’s architecture. In addition to the above-mentioned technical aspects of software architecture m- els, non-technical aspects, especially project management-related activities, require an explicit software architecture model. The models are used as input for cost esti- tions, time-, deadline-, and resource planning for the development teams. They serve the project management activities of planning, executing, and controlling, which are necessary to deliver high-quality software systems in time and within the budget.

This book constitutes the refereed proceedings of the Second European Conference on Software Architecture, ECSA 2008, held in Paphos, Cyprus, in September/October 2008. The 12 revised full papers presented together with 2 keynote abstracts, 4 experience papers, 7 emerging research papers, and 12 research challenge poster papers were carefully reviewed and selected from 83 submissions. The papers focus on formalisms, technologies, and processes for describing, verifying, validating, transforming, building, and evolving software systems. Topics include architecture modeling, architecture description languages, architectural aspects, architecture analysis, transformation and synthesis, architecture evolution, quality attributes, model-driven engineering, built-in testing and architecture-based support for component-based and service-oriented systems.

This book explores inductive inference using the minimum message length (MML) principle, a Bayesian method which is a realisation of Ockham's Razor based on information theory. Accompanied by a library of software, the book can assist an applications programmer, student or researcher in the fields of data analysis and machine learning to write computer programs based upon this principle. MML inference has been around for 50 years and yet only one highly technical book has been written about the subject. The majority of research in the field has been backed by specialised one-off programs but this book includes a library of general MML–based software, in Java. The Java source code is available under the GNU GPL open-source license. The software library is documented using Javadoc which produces extensive cross referenced HTML manual pages. Every probability distribution and statistical model that is described in the book is implemented and documented in the software library. The library may contain a component that directly solves a reader's inference problem, or contain components that can be put together to solve the problem, or provide a standard interface under which a new component can be written to solve the problem. This book will be of interest to application developers in the fields of machine learning and statistics as well as academics, postdocs, programmers and data scientists. It could also be used by third year or fourth year undergraduate or postgraduate students.

This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.