Scalable Inference in Graph-coupled Continuous-time Markov Chains
In this dissertation novel techniques for inference and learning of and decision-making in probabilistic graphical models over combinatorial state-spaces in continuous-time are developed. Such models are prevalent in the natural sciences and engineering. They can be used to describe various types of...
Internet
https://tuprints.ulb.tu-darmstadt.de/17403/7/2020-29-12_Linzner_Dominik.pdfLinzner, Dominik <http://tuprints.ulb.tu-darmstadt.de/view/person/Linzner=3ADominik=3A=3A.html> (2021): Scalable Inference in Graph-coupled Continuous-time Markov Chains. (Publisher's Version)Darmstadt, Technische Universität, DOI: 10.12921/tuprints-00017403 <https://doi.org/10.12921/tuprints-00017403>, [Ph.D. Thesis]