Format:
1 Online-Ressource (vii, 129 Seiten, 13688 KB)
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Illustrationen, Diagramme
Content:
Point processes are a common methodology to model sets of events. From earthquakes to social media posts, from the arrival times of neuronal spikes to the timing of crimes, from stock prices to disease spreading -- these phenomena can be reduced to the occurrences of events concentrated in points. Often, these events happen one after the other defining a time--series. Models of point processes can be used to deepen our understanding of such events and for classification and prediction. Such models include an underlying random process that generates the events. This work uses Bayesian methodology to infer the underlying generative process from observed data. Our contribution is twofold -- we develop new models and new inference methods for these processes. We propose a model that extends the family of point processes where the occurrence of an event depends on the previous events. This family is known as Hawkes processes. Whereas in most existing models of such processes, past events are assumed to have only an excitatory effect ...
Note:
Dissertation Universität Potsdam 2023
Additional Edition:
Erscheint auch als Druck-Ausgabe Malem-Shinitski, Noa Bayesian inference and modeling for point processes with applications from neuronal activity to scene viewing Potsdam, 2023
Language:
English
Keywords:
Hochschulschrift
DOI:
10.25932/publishup-61495
URN:
urn:nbn:de:kobv:517-opus4-614952
