Stochastic Process Models On Dynamic Networks
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Stochastic Process Models on Dynamic Networks
We present novel model frameworks and inference procedures for stochastic point processes on dynamic networks. The point process can be defined for a random phenomenon that spreads among the network nodes, and for the temporally evolving network itself. Methods development is motivated by the needs of health and social science data, where partial observations or latent structures are common and create challenges to likelihood-based inference. In this dissertation, we will discuss parameter estimation techniques that can handle these latent variables and make effective use of the complete data likelihood for efficient inference. We start with developing individualized continuous time Markov chain models for stochastic epidemics on a dynamic contact network. Data-augmentation algorithms are designed to address partial observations (such as missing infection and recovery times) in epidemic data while accommodating the network dynamics. We apply the frameworks to the study of non-pharmaceutical interventions in a college population. Next, we move on to study the higher-order latent structures of dynamic inter-personal interactions by combining a multi-resolution spatio-temporal stochastic process with a latent factor model for a dynamic social network. We apply it to analyzing basketball data where the discovered latent structure defines a metric that helps evaluate the quality of game play. Finally, we discuss extensions to a non-Markovian setting of self and mutually exciting point processes (Hawkes processes). We utilize the branching structure of the Hawkes processes to uncover the latent replying structure of a group conversation, which can be further employed to quantitatively measure individual social impact.
Simulating Social Phenomena
In this book experts from quite different fields present simulations of social phenomena: economists, sociologists, political scientists, psychologists, cognitive scientists, organisational scientists, decision scientists, geographers, computer scientists, AI and AL scientists, mathematicians and statisticians. They simulate markets, organisations, economic dynamics, coalition formation, the emergence of cooperation and exchange, bargaining, decision making, learning, and adaptation. The history, problems, and perspectives of simulating social phenomena are explicitly discussed.
Dynamics and Stochasticity in Transportation Systems
Dynamics and Stochasticity in Transportation Systems: Solutions for Transportation Network Modeling breaks new ground on the topics, providing consistent and comprehensive coverage of steady state equilibrium and dynamic assignment within a common strategy. The book details the most recent advances in network assignment, including day-to-day and within-day dynamics, providing a solid foundation to help transportation planners solve transient overload and other problems. Users will find a book that fills the gap in knowledge with its description on how to use and employ the latest dynamic network models for evaluation of traffic and transport demand interventions. This book demystifies the many different dynamic traffic assignment approaches and requires no previous knowledge on the part of the reader. All results are fully described and proven, thus eliminating the need to seek out other references. The skills described will appeal to transportation professionals, researchers and graduate students alike. Presents a consistent and comprehensive theory on steady state equilibrium assignment and day-to-day dynamic assignment models within a common framework Describes and solves modeling calculations in detail, with no need to reference other sources Includes numerical and graphical examples, text boxes and summaries at the end of each chapter to help readers better understand theoretical components Includes primary mathematical tools necessary for each dynamic model, easing comprehension