markov chains

Bayesian Inference for Partially Observed Continuous-Time Processes

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B5 L5 R5220

This thesis develops Bayesian inference methods for partially observed stochastic differential equations (SDEs) with unknown parameters, focusing on the stochastic Volterra equation (SVE), non-synchronous diffusions, and McKean-Vlasov SDEs. Employing Euler-Maruyama discretization.

A talk by Dr. Chiheb Ben Hammouda in the Monte Carlo & Quasi-Monte Carlo (MCQMC) Methods in Scientific Computing Conference, in August 2020, University of Oxford

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​Dr. Chiheb Ben Hammouda gave, on August 11, 2020, a talk entitled "Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks" in the Monte Carlo & Quasi-Monte Carlo (MCQMC) Methods in Scientific Computing Conference, 10-14 August 2020, University of Oxford.​​​​

Evangelia Kalligiannaki

Evangelia Kalligiannaki worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Evangelia's research interests concerned the development, analysis, and implementation of model reduction (coarse-graining) techniques for high dimensional particle systems at multiple spaces and time scales, and included studying macro-molecular and stochastic lattice systems. She was particularly interested in the development of effective equilibrium and non-equilibrium coarse-grained models