System identification using autoregressive Bayesian neural networks with nonparametric noise models
From MaRDI portal
Publication:6135347
DOI10.1111/jtsa.12669arXiv2104.12119OpenAlexW4306697787MaRDI QIDQ6135347
Christos Merkatas, Simo Särkkä
Publication date: 24 August 2023
Published in: Journal of Time Series Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2104.12119
system identificationBayesian nonparametricsnonlinear time seriesinfinite mixture modelsgeometric stick breaking
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Inference from stochastic processes (62Mxx)
Cites Work
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- Time series forecasting using a hybrid ARIMA and neural network model
- Additive cubic spline regression with Dirichlet process mixture errors
- System identification. An introduction.
- Geometric stick-breaking processes for continuous-time Bayesian nonparametric modeling
- Embedding and learning with signatures
- Bayesian estimation of chaotic signals generated by piecewise-linear maps
- Bayesian reconstructions and predictions of nonlinear dynamical systems via the hybrid Monte Carlo scheme
- A Bayesian nonparametric study of a dynamic nonlinear model
- A nonparametric dependent process for Bayesian regression
- Forecasting nonlinear time series with a hybrid methodology
- Bayes inference in regression models with ARMA\((p,q)\) errors
- Dependent mixture models: clustering and borrowing information
- Dependent mixtures of geometric weights priors
- Chaos-based digital communication systems. Operating principles, analysis methods, and performance evaluation
- Nonlinear black-box modeling in system identification: A unified overview
- Bayesian learning for neural networks
- A survey of Bayesian predictive methods for model assessment, selection and comparison
- On the inferential implications of decreasing weight structures in mixture models
- Spike and slab variable selection: frequentist and Bayesian strategies
- Optimal tuning of the hybrid Monte Carlo algorithm
- Finite mixture and Markov switching models.
- Time Series Modeling of Neuroscience Data
- A New Bayesian Nonparametric Mixture Model
- The horseshoe estimator for sparse signals
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- Neural networks for nonlinear dynamic system modelling and identification
- Joint reconstruction and prediction\break of random dynamical systems under\break borrowing of strength
- A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems
- Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
- Reconstructions and predictions of nonlinear dynamical systems: a hierarchical Bayesian approach
- Dynamic systems identification with Gaussian processes
- A new look at the statistical model identification
