Toward Integration Of Bayesian Networks With Geographic Information Systems And Complex Systems Theory For Urban Land Use Change Modelling

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Toward Integration of Bayesian Networks with Geographic Information Systems and Complex Systems Theory for Urban Land Use Change Modelling

Author : Verda Kocabas Ersahin
Publisher :
ISBN 13 :
Total Pages : 550 pages
Book Rating : 4.:/5 (112 download)

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Book Synopsis Toward Integration of Bayesian Networks with Geographic Information Systems and Complex Systems Theory for Urban Land Use Change Modelling by : Verda Kocabas Ersahin

Download or read book Toward Integration of Bayesian Networks with Geographic Information Systems and Complex Systems Theory for Urban Land Use Change Modelling written by Verda Kocabas Ersahin and published by . This book was released on 2008 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human-initiated land use change is the most significant factor behind the loss of agricultural and forested areas, thus global climate change. It is important to understand the reasons behind land use decisions as it is to understand their consequences. Empirical observations and controlled experimentation are not usually feasible methods for studying this change. Therefore, researchers have employed complex systems theory (or complexity theory) to help them understand and model dynamic land use change process in cities. Cellular automata (CA) theory and agent-based modeling have widely applied in land use change modelling. CA models can easily model spatial process that is changing over time, and can handle fine scale dynamics of these spatial processes. Agent-based models (ABMs) excel at relating the heterogeneous behaviour of agents with different information, different decision rules, and different situation to the macro behaviour of the overall system. While both have advantages, they have a number of challenges when applied to land use change. One of the aims of this dissertation is to develop novel modelling approaches that integrate geographic information systems (GIS), CA and ABMs with Bayesian Networks (BNs) for overcoming limitations in the modelling process by significantly reducing the tedious work in defining parameter values, transition rules and model structures. As the use of land use models in planning is not widely accepted and not trusted fully by the urban planners, the other aim is to link land use models with planning support systems (PSS), especially to use enhanced ABMs in PSS. Therefore, the proposed modelling approaches were applied to assist in understanding the patterns and controls of land use change both spatially and temporarily for the Metro Vancouver region. They were used to analyze the effects of planning decisions in accordance with the sustainable development point of view.

Environment and Planning

Author :
Publisher :
ISBN 13 :
Total Pages : 1184 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Environment and Planning by :

Download or read book Environment and Planning written by and published by . This book was released on 2009 with total page 1184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Benefits of Bayesian Network Models

Author : Philippe Weber
Publisher : John Wiley & Sons
ISBN 13 : 1119347459
Total Pages : 146 pages
Book Rating : 4.1/5 (193 download)

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Book Synopsis Benefits of Bayesian Network Models by : Philippe Weber

Download or read book Benefits of Bayesian Network Models written by Philippe Weber and published by John Wiley & Sons. This book was released on 2016-08-23 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Unfortunately, this modeling formalism is not fully accepted in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty. This book explains the principles of knowledge structuration to ensure a valid BN and DBN model and illustrate the flexibility and efficiency of these representations in dependability, risk analysis and control of multi-state systems and dynamic systems. Across five chapters, the authors present several modeling methods and industrial applications are referenced for illustration in real industrial contexts.

Advancing Land Change Modeling

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309288363
Total Pages : 267 pages
Book Rating : 4.3/5 (92 download)

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Book Synopsis Advancing Land Change Modeling by : National Research Council

Download or read book Advancing Land Change Modeling written by National Research Council and published by National Academies Press. This book was released on 2014-03-31 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: People are constantly changing the land surface through construction, agriculture, energy production, and other activities. Changes both in how land is used by people (land use) and in the vegetation, rock, buildings, and other physical material that cover the Earth's surface (land cover) can be described and future land change can be projected using land-change models (LCMs). LCMs are a key means for understanding how humans are reshaping the Earth's surface in the past and present, for forecasting future landscape conditions, and for developing policies to manage our use of resources and the environment at scales ranging from an individual parcel of land in a city to vast expanses of forests around the world. Advancing Land Change Modeling: Opportunities and Research Requirements describes various LCM approaches, suggests guidance for their appropriate application, and makes recommendations to improve the integration of observation strategies into the models. This report provides a summary and evaluation of several modeling approaches, and their theoretical and empirical underpinnings, relative to complex land-change dynamics and processes, and identifies several opportunities for further advancing the science, data, and cyberinfrastructure involved in the LCM enterprise. Because of the numerous models available, the report focuses on describing the categories of approaches used along with selected examples, rather than providing a review of specific models. Additionally, because all modeling approaches have relative strengths and weaknesses, the report compares these relative to different purposes. Advancing Land Change Modeling's recommendations for assessment of future data and research needs will enable model outputs to better assist the science, policy, and decisionsupport communities.

Guide to Programs of Geography in the United States and Canada

Author :
Publisher :
ISBN 13 :
Total Pages : 700 pages
Book Rating : 4.:/5 (321 download)

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Book Synopsis Guide to Programs of Geography in the United States and Canada by :

Download or read book Guide to Programs of Geography in the United States and Canada written by and published by . This book was released on 2008 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Bayesian Networks in Educational Assessment

Author : Russell G. Almond
Publisher : Springer
ISBN 13 : 1493921258
Total Pages : 678 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Bayesian Networks in Educational Assessment by : Russell G. Almond

Download or read book Bayesian Networks in Educational Assessment written by Russell G. Almond and published by Springer. This book was released on 2015-03-10 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.

Enhanced Bayesian Network Models for Spatial Time Series Prediction

Author : Monidipa Das
Publisher : Springer Nature
ISBN 13 : 3030277496
Total Pages : 149 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Enhanced Bayesian Network Models for Spatial Time Series Prediction by : Monidipa Das

Download or read book Enhanced Bayesian Network Models for Spatial Time Series Prediction written by Monidipa Das and published by Springer Nature. This book was released on 2019-11-07 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

Author : Uffe B. Kjærulff
Publisher : Springer Science & Business Media
ISBN 13 : 1461451043
Total Pages : 388 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis by : Uffe B. Kjærulff

Download or read book Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis written by Uffe B. Kjærulff and published by Springer Science & Business Media. This book was released on 2012-11-30 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.

Bayesian Networks and Decision Graphs

Author : Thomas Dyhre Nielsen
Publisher : Springer Science & Business Media
ISBN 13 : 0387682821
Total Pages : 457 pages
Book Rating : 4.3/5 (876 download)

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Book Synopsis Bayesian Networks and Decision Graphs by : Thomas Dyhre Nielsen

Download or read book Bayesian Networks and Decision Graphs written by Thomas Dyhre Nielsen and published by Springer Science & Business Media. This book was released on 2009-03-17 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Bayesian Networks

Author : Olivier Pourret
Publisher : John Wiley & Sons
ISBN 13 : 9780470994542
Total Pages : 446 pages
Book Rating : 4.9/5 (945 download)

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Book Synopsis Bayesian Networks by : Olivier Pourret

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.

Probabilistic Networks and Expert Systems

Author : Robert G. Cowell
Publisher : Springer Science & Business Media
ISBN 13 : 0387226303
Total Pages : 324 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Probabilistic Networks and Expert Systems by : Robert G. Cowell

Download or read book Probabilistic Networks and Expert Systems written by Robert G. Cowell and published by Springer Science & Business Media. This book was released on 2006-05-29 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.

Ecosystem and Territorial Resilience

Author : Emmanuel Garbolino
Publisher : Elsevier
ISBN 13 : 0128182164
Total Pages : 402 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Ecosystem and Territorial Resilience by : Emmanuel Garbolino

Download or read book Ecosystem and Territorial Resilience written by Emmanuel Garbolino and published by Elsevier. This book was released on 2020-09-15 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecosystem and Territorial Resilience: A Geoprospective Approach provides a full review of the geoprospective approach and how it can be used in planning for and implementing environmental and territorial resilience measures. The geoprospective approach is a way to predict and assess for future risks, and is a comprehensive method for identifying and addressing potential change impacts. In addition to the main concepts and methods of this approach, the book presents applications and case studies for different spatio-temporal scales and problems related to the degradation of socio-ecosystems, as well as applying the geoprospective approach to environmental and urban planning.The book offers an interdisciplinary perspective, tying in concepts and techniques from geography, including spatial analysis methods, modelling, and GIS, to address issues of ecological impacts of climate change, urban risk and resilience, land use changes, coastal impacts, and sustainable development and potential of adaptability. This book is a unique and integral resource for policy makers, environmental and territorial managers, scientists, engineers, consultants, and graduate students interested in anticipating future change in socio-ecosystems. Introduces the geoprospective approach to assess the impact of global changes on socio-ecosystems, and potential risk situations for ecosystems and society Includes geographical techniques such as spatial analysis methods, modeling, and GIS to address various climate change issues and to detect vulnerabilities vs adaptive capacities of spatial systems Provides case studies as well as interviews with planners and policy makers regarding their views on territorial planning and expectations of the geoprospective

The Science of Cities and Regions

Author : Alan Wilson
Publisher : Springer Science & Business Media
ISBN 13 : 9400722664
Total Pages : 108 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis The Science of Cities and Regions by : Alan Wilson

Download or read book The Science of Cities and Regions written by Alan Wilson and published by Springer Science & Business Media. This book was released on 2012-01-05 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: A ‘science of cities and regions’ is critical for meeting future challenges. The world is urbanising: huge cities are being created and are continuing to grow rapidly. There are many planning and development issues arising in different manifestations in countries across the globe. These developments can, in principle, be simulated through mathematical computer models which provide tools for forecasting and testing future scenarios and plans. These models can represent the functioning of cities and regions, predicting the spatial demography and the economy, the main flows such as journey to work or to services, and the mechanisms of future evolution. In this book, the main principles involved in the design of this range of models are articulated, providing an account of the current state of the art as well as future research challenges. Alan Wilson has over forty years working with urban and regional models and has contributed important discoveries. He has distilled this experience into what serves as both an introduction and a review of the research frontier. Topics covered include the Lowry model, the retail model, principles of account-based models and the methods rooted in Boltzmann-style statistical modelling and the Lotka-Volterra approach to system evolution. Applications range from urban and regional planning to wars and epidemics.

Bayesian Networks in R

Author : Radhakrishnan Nagarajan
Publisher : Springer Science & Business Media
ISBN 13 : 1461464463
Total Pages : 168 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Bayesian Networks in R by : Radhakrishnan Nagarajan

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.

Bayesian Network

Author : Ahmed Rebai
Publisher : BoD – Books on Demand
ISBN 13 : 9533071249
Total Pages : 446 pages
Book Rating : 4.5/5 (33 download)

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Book Synopsis Bayesian Network by : Ahmed Rebai

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.

Bayesian Network Technologies: Applications and Graphical Models

Author : Mittal, Ankush
Publisher : IGI Global
ISBN 13 : 159904143X
Total Pages : 368 pages
Book Rating : 4.5/5 (99 download)

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Book Synopsis Bayesian Network Technologies: Applications and Graphical Models by : Mittal, Ankush

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.

Gated Bayesian Networks

Author : Marcus Bendtsen
Publisher : Linköping University Electronic Press
ISBN 13 : 9176855252
Total Pages : 245 pages
Book Rating : 4.1/5 (768 download)

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Book Synopsis Gated Bayesian Networks by : Marcus Bendtsen

Download or read book Gated Bayesian Networks written by Marcus Bendtsen and published by Linköping University Electronic Press. This book was released on 2017-06-08 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.