Bayesian Inference and Forecasting in Dynamic Neural Networks with Fully Markov Switching ARCH Noises
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Publication:3526064
DOI10.1080/03610920701851805zbMath1143.62055OpenAlexW1991609144MaRDI QIDQ3526064
Publication date: 24 September 2008
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610920701851805
Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Monte Carlo methods (65C05) Neural nets and related approaches to inference from stochastic processes (62M45)
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Cites Work
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- Optimization by Simulated Annealing
- Analysis of time series subject to changes in regime
- Calculating posterior distributions and modal estimates in Markov mixture models
- Bayesian model averaging: A tutorial. (with comments and a rejoinder).
- Generalized autoregressive conditional heteroscedasticity
- Bayesian learning for neural networks
- Robust Full Bayesian Learning for Radial Basis Networks
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
- DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
- On Gibbs sampling for state space models
- Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models
- Marginal Likelihood From the Metropolis–Hastings Output
- Bayesian analysis of switching ARCH models
- Approximation by superpositions of a sigmoidal function
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