|Title:||Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing|
|Subject (DDC):||003: Systems|
|Abstract:||Parameter inference is a fundamental problem in data-driven modeling. The aim is to find a so-called posterior distribution of model parameters that are able to explain observed data and can be used for making probabilistic predictions. We propose a novel, exact, very efficient and highly parallelizable Hamiltonian Monte Carlo approach for generating posterior parameter distributions, for stochastic models calibrated to measured time-series. The algorithm is inspired by re-interpreting the posterior distribution as a statistical mechanics partition function of an object akin to a polymer, whose dynamics is confined by both the model and the measurements.|
|Further description:||Invited seminar at ENS, Paris|
|License (according to publishing contract):||Not specified|
|Departement:||Life Sciences and Facility Management|
|Organisational Unit:||Institute of Computational Life Sciences (ICLS)|
|Appears in collections:||Publikationen Life Sciences und Facility Management|
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