About: MCMC for Bayesian Uncertainty Quantification from Time-Series Data

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	MCMC for Bayesian Uncertainty Quantification from Time-Series Data
Creator	Naumann, Uwe Peltzer, Patrick Bojak, Ingo Maybank, Philip
Source	PMC
abstract	In computational neuroscience, Neural Population Models (NPMs) are mechanistic models that describe brain physiology in a range of different states. Within computational neuroscience there is growing interest in the inverse problem of inferring NPM parameters from recordings such as the EEG (Electroencephalogram). Uncertainty quantification is essential in this application area in order to infer the mechanistic effect of interventions such as anaesthesia. This paper presents [Image: see text] software for Bayesian uncertainty quantification in the parameters of NPMs from approximately stationary data using Markov Chain Monte Carlo (MCMC). Modern MCMC methods require first order (and in some cases higher order) derivatives of the posterior density. The software presented offers two distinct methods of evaluating derivatives: finite differences and exact derivatives obtained through Algorithmic Differentiation (AD). For AD, two different implementations are used: the open source Stan Math Library and the commercially licenced [Image: see text] tool distributed by NAG (Numerical Algorithms Group). The use of derivative information in MCMC sampling is demonstrated through a simple example, the noise-driven harmonic oscillator. And different methods for computing derivatives are compared. The software is written in a modular object-oriented way such that it can be extended to derivative based MCMC for other scientific domains.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50436-6_52
has license	no-cc
sha1sum (hex)	817267a297cf8314e0f9a4bc19c259350cfd35d6
schema:url	https://doi.org/10.1007/978-3-030-50436-6_52
resource representing a document's title	MCMC for Bayesian Uncertainty Quantification from Time-Series Data
has PubMed Central identifier	PMC7304777
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:817267a297cf8314e0f9a4bc19c259350cfd35d6#body_text
is schema:about of	named entity 'NPM' named entity 'mechanistic' named entity 'Bayesian' named entity 'INTEREST' named entity 'INFER' named entity 'infer' named entity 'Uncertainty Quantification' named entity 'computational neuroscience' named entity 'EEG' named entity 'anaesthesia' named entity 'computational neuroscience' named entity 'Uncertainty quantification' named entity 'white noise' named entity 'credible intervals' named entity 'computationally efficient' named entity 'c++' named entity 'c++' named entity 'state-space' named entity 'probability distribution' named entity 'MCMC' named entity 'fixed point' named entity 'MCMC' named entity 'computing' named entity 'posterior distribution' named entity 'MCMC' named entity 'Stan Math Library' named entity 'MCMC' named entity 'Monte Carlo' named entity 'complex variables' named entity 'RWTH Aachen University' named entity 'credible intervals' named entity 'Kalman' named entity 'convergence rate' named entity 'observed data' named entity 'state-space' named entity 'dot product' named entity 'state-space' named entity 'credible intervals' named entity 'spectral density' named entity 'MCMC' named entity 'typedef' named entity 'MCMC' named entity 'sampling' named entity 'typedef' named entity 'eigenvector' named entity 'C++' named entity 'Welch method' named entity 'sampling' named entity 'MCMC' named entity 'probability density' named entity 'spectral density' named entity 'Bayesian' named entity 'computational neuroscience' named entity 'eval' named entity 'MCMC' named entity 'j++' named entity 'computationally efficient' named entity 'Bayesian' named entity 'finite differences' named entity 'physical model' named entity 'credible intervals' named entity 'C++' named entity 'spectral density' named entity 'modelling language' named entity 'Hamiltonian Monte Carlo' named entity 'posterior density' named entity 'confidence intervals' named entity 'diagonal matrix' named entity 'ith' named entity 'first-order' named entity 'uncertainty quantification' named entity 'autocorrelation' named entity 'HMC' named entity 'linear algebra' named entity 'differentiation' named entity 'finite difference approximation' named entity 'equilibrium point' named entity 'spectral density' named entity 'computation time' named entity 'sampling'

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