Title: Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing
Authors : Ulzega, Simone
Issue Date: 11-Apr-2017
Language : Englisch / English
Subject (DDC) : 003: Systeme / 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
Departement: Life Sciences und Facility Management
Organisational Unit: Institut für Angewandte Simulation (IAS)
Publication type: Vorlesung / Lecture
URI: https://digitalcollection.zhaw.ch/handle/11475/7743
License (according to publishing contract) : Keine Angabe / Not specified
Appears in Collections:Publikationen Life Sciences und Facility Management

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