Bayesian parameter estimation and model selection for strongly nonlinear dynamical systems

DOI10.1007/s11071-015-2217-8zbMath1437.62106OpenAlexW938483330MaRDI QIDQ330721

Mohammad Khalil, Philippe Bisaillon, Rimple Sandhu, Dominique Poirel, Abhijit Sarkar, Chris Pettit

Publication date: 26 October 2016

Published in: Nonlinear Dynamics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11071-015-2217-8

zbMATH Keywords

Bayesian inferenceBayesian model selectionparticle filterensemble Kalman filter

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Point estimation (62F10) Bayesian inference (62F15)

Related Items (6)

Bayesian inference of nonlinear unsteady aerodynamics from aeroelastic limit cycle oscillations ⋮ A parallel time integrator for noisy nonlinear oscillatory systems ⋮ Bayesian parameter estimation and model selection for strongly nonlinear dynamical systems ⋮ Model selection and parameter estimation for an improved approximate Bayesian computation sequential Monte Carlo algorithm ⋮ Bayesian synergistic metamodeling (BSM) for physical information infused data-driven metamodeling ⋮ Spectral method and Bayesian parameter estimation for the space fractional coupled nonlinear Schrödinger equations

Uses Software

• EnKF
• BayesDA

Cites Work

• Adaptive proposal distribution for random walk Metropolis algorithm
• Bayesian parameter estimation and model selection for strongly nonlinear dynamical systems
• Nonlinear filters for chaotic oscillatory systems
• Nonlinear oscillations, dynamical systems, and bifurcations of vector fields
• Estimating the dimension of a model
• Bayesian model selection for nonlinear aeroelastic systems using wind-tunnel data
• Statistical and computational inverse problems.
• Bayesian model selection and model averaging
• Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation
• Sequential Imputations and Bayesian Missing Data Problems
• Bayesian Measures of Model Complexity and Fit
• Marginal Likelihood From the Metropolis–Hastings Output
• Bayes Factors
• The Bayesian Choice
• Introducing Monte Carlo Methods with R
• Data Assimilation
• An adaptive Metropolis algorithm
• A new look at the statistical model identification
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Bayesian parameter estimation and model selection for strongly nonlinear dynamical systems