markov chains

Manifold Markov Chain Monte Carlo Methods for Bayesian Inference In Diffusion Models

Prof. Alexandros Beskos, the Dept of Statistical Science, University College London (UCL)

Nov 10, 15:00 - 16:00

KAUST

markov chains bayesian inference nonlinearity

Bayesian inference for nonlinear diffusions, observed at discrete times,is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology - borrowing ideas from statistical physics and computational chemistry - for inferring the posterior distribution of latent diffusion paths and model parameters, given observations of the process

Evangelia Kalligiannaki

Research Scientist, Stochastic Numerics Research Group

numerical analysis markov chains uncertainty quantification

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