| Publication type: | Lecture |
| Title: | Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing |
| Authors: | Ulzega, Simone |
| Issue Date: | 11-Apr-2017 |
| Language: | English |
| 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 |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/7743 |
| 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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Ulzega, S. (2017). Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing.
Ulzega, S. (2017) Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing.
S. Ulzega, Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing. 2017.
ULZEGA, Simone, 2017. Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing
Ulzega, Simone. 2017. Boosting Parameter Inference with Stochastic Models Using Molecular Dynamics and High-Performance Computing.
Ulzega, Simone. Boosting Parameter Inference with Stochastic Models Using Molecular Dynamics and High-Performance Computing. 2017.
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