Bayesian model selection using automatic relevance determination for nonlinear dynamical systems
DOI10.1016/j.cma.2017.01.042zbMath1439.62087OpenAlexW2587831136MaRDI QIDQ2309843
Dominique Poirel, Rimple Sandhu, Chris Pettit, Abhijit Sarkar, Mohammad Khalil
Publication date: 6 April 2020
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2017.01.042
Kalman filterBayesian model selectionMarkov chain Monte Carlo simulationautomatic relevance determination
Bayesian inference (62F15) Filtering in stochastic control theory (93E11) Dynamical systems in solid mechanics (37N15) Nonlinear dynamics in mechanics (70K99) Dynamical systems in control (37N35) Dynamical systems in classical and celestial mechanics (37N05)
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Kalman filtering with real-time applications
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- On Metropolis-Hastings algorithms with delayed rejection
- Bayesian model selection for nonlinear aeroelastic systems using wind-tunnel data
- Inference from iterative simulation using multiple sequences
- Bayesian learning for neural networks
- Bayesian inference of nonlinear unsteady aerodynamics from aeroelastic limit cycle oscillations
- Uncertainty quantification of limit-cycle oscillations
- Bayesian Calibration of Computer Models
- Learn From Thy Neighbor: Parallel-Chain and Regional Adaptive MCMC
- Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation
- Marginal Likelihood from the Gibbs Output
- Information Theory and Statistical Mechanics
- Marginal Likelihood Estimation via Power Posteriors
- Accurate Approximations for Posterior Moments and Marginal Densities
- 10.1162/15324430152748236
- Marginal Likelihood From the Metropolis–Hastings Output
- Bayes Factors
- Equation of State Calculations by Fast Computing Machines
- Monte Carlo sampling methods using Markov chains and their applications
- Data Assimilation
- An adaptive Metropolis algorithm
- Nested sampling for general Bayesian computation