Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 9 of 9 for “"Hawkes processes"”.
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Hawkes processes and some financial applications
… process, which is now more commonly known as the Hawkes process, is a model for a point process on the real line introduced by Hawkes (1971). The distinguishing feature of such processes is that they allow all past `events' to affect the intensity function at the current time. Over the years such …
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Parameter Estimation for Anonymous Hawkes Processes
Hawkes Processes are self-exciting point processes used to model many real-life networks in which an event from one agent causes the rate at which events occur from related agents to increase, such as in earthquake networks or social media. This project investigates the question of finding the …
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New Spatio-temporal Hawkes Process Models For Social Good
… across events is also getting stronger. Hawkes processes stand out given their ability to capture a wide range of contagion and self-excitation patterns, including the transmission of infectious disease, earthquake aftershock distributions, near-repeat crime patterns, and overdose …
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Market Simulations with a Matching Engine
… via two seperate simulation techniques — Hawkes processes and agent-based modelling. A 10-variate Hawkes model stress tests the software whilst measuring the extent to which a matching engine can cloud the modelling of underlying order submission and management processes in a …
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Optimizing resource allocation in computational sustainability: Models, algorithms and tools
… network cascades modeled with multivariate Hawkes processes. In conjunction, we derive an exact optimization approach for the proposed intervention based on closed-form expressions of the objective functions, which is applicable in a broad swath of domains beyond invasive species, such as …
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Efficient learning of temporal dynamics with first-order methods
… to model high-dimensional data generating processes. For instance, event data are often modeled with point processes, while time series data are often captured by autoregressive models or differential equations. In this dissertation, we design algorithms for such models that enable …
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Causal structure of networks of stochastic processes
… and functional dependencies in network of processes. Statistical dependencies can be encoded by directed information graphs (DIGs) and functional relationships using functional dependency graphs (FDGs), both of which are graphical models where nodes represent random processes. DIGs are …
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Modeling, predicting, and guiding users' temporal behaviors
… nonparametric learning of temporal point processes, (2) a generic embedding framework for continuous-time evolving graphs, (3) scalable algorithms for predicting user activity levels, and (4) a stochastic differential equation framework for guiding users’ activities.