Enhanced Bayesian Network Models For Spatial Time Series Prediction

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.

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.

This book presents the select proceedings of the Virtual Conference on Disaster Risk Reduction (VCDRR 2021). It emphasizes on the role of civil engineering for a disaster resilient society. It presents latest research on climate change and water security focusing on disaster risk reduction. Various topics covered in this book are climate change, stormwater management, flood risk analysis, drought management, water treatment, etc. This book is a comprehensive volume on disaster risk reduction (DRR) and its management for a sustainable built environment. This book is useful for the students, researchers, policy makers and professionals working in the area of civil engineering, climate change and disaster management.

Artificial Intelligence continues to be one of the most exciting and fast-developing fields of computer science. This book presents the 177 long papers and 123 short papers accepted for ECAI 2016, the latest edition of the biennial European Conference on Artificial Intelligence, Europe’s premier venue for presenting scientific results in AI. The conference was held in The Hague, the Netherlands, from August 29 to September 2, 2016. ECAI 2016 also incorporated the conference on Prestigious Applications of Intelligent Systems (PAIS) 2016, and the Starting AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume; the papers from STAIRS are published in a separate volume in the Frontiers in Artificial Intelligence and Applications (FAIA) series. Organized by the European Association for Artificial Intelligence (EurAI) and the Benelux Association for Artificial Intelligence (BNVKI), the ECAI conference provides an opportunity for researchers to present and hear about the very best research in contemporary AI. This proceedings will be of interest to all those seeking an overview of the very latest innovations and developments in this field.

Cairo is experiencing traffic congestion that places it among the worst in the world. Obviously, it is difficult if not impossible to solve the transportation problem because it is multi-dimensional problem but it's good to reduce this waste of money and the associated waste of time resulting from congestion.

This book presents the thoroughly refereed and revised post-workshop proceedings of the 19th Monterey Workshop, held in Beijing, China, in Ocotber 2016. The workshop explored the challenges associated with the Development, Operation and Management of Large-Scale complex IT Systems. The 18 revised full papers presented were significantly extended and improved by the insights gained from the productive and lively discussions at the workshop, and the feedback from the post-workshop peer reviews. 2016 marks the 23rd anniversary for the Monterey Workshop series which started in 1993. For nearly a quarter of century, the Monterey Workshops have established themselves as an important international forum to foster, among academia, industry, and government agencies, discussion and exchange of ideas, research results and experience in developing software intensive systems, and have significantly advanced the field. The community of the workshop participants has grown to become an influential source of ideas and innovations and its impact on the knowledge economy has been felt worldwide.

This book includes selected contributions related to big data and data networking, presented at the 13th International Conference on Computing and Information Technology (IC2IT), which was held at the Arnoma Grand Hotel Bangkok, Thailand, July 6–7, 2017. The aim of the conference was to present emerging algorithms, methods and technologies with a high degree of originality, novelty and innovation addressing the conference theme `Mastering Data and Networking’. Section 1 and 2 discuss various aspects of data mining and corresponding applications. Section 3 focuses on speed and overhead networking optimisation problems, as well as energy problems of autonomous systems, which are becoming increasingly important. The key to addressing these problems is properly determining critical parameters. Section 4 sheds light on natural language processing, including extraction of trends and popularity and recognition of emotions as well as classic topics such as detection and classification.

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.