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- W3025335824 abstract "In this thesis, a comprehensive benchmark study about Markov chain Monte Carlo (MCMC) is implemented and presented based on a novel, fair analysis framework. The lessons learned during this evaluation are then used to construct a novel MCMC method. The algorithm facilitates the strengths of multiple existing algorithms. The novel method is shown to be capable of outperforming other state-of-the-art methods while maintaining strong self-tuning capabilities. This effort enables an straightforward, efficient and robust application of state-of-the-art MCMC across many biological applications and aids the future development of MCMC in systems biology." @default.
- W3025335824 created "2020-05-21" @default.
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- W3025335824 date "2019-01-01" @default.
- W3025335824 modified "2023-09-27" @default.
- W3025335824 title "Development and Evaluation of Sampling-based Parameter Estimation Methods for Dynamic Biological Processes" @default.
- W3025335824 hasPublicationYear "2019" @default.
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