Neural Network Models for Conditional Distribution Under Bayesian Analysis
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Publication:5446247
DOI10.1162/neco.2007.3182zbMath1146.68426OpenAlexW2090590581WikidataQ51899591 ScholiaQ51899591MaRDI QIDQ5446247
Tatiana Miazhynskaia, Georg Dorffner, Sylvia Frühwirth-Schnatter
Publication date: 6 March 2008
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1162/neco.2007.3182
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Cites Work
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- A comparison of Bayesian model selection based on MCMC with an application to GARCH-type models
- Exact predictive densities for linear models with ARCH disturbances
- Generalized autoregressive conditional heteroscedasticity
- Bayesian analysis of ARMA-GARCH models: a Markov chain sampling approach
- Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques*
- Using simulation methods for bayesian econometric models: inference, development,and communication
- Dealing With Label Switching in Mixture Models
- Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities
- Marginal Likelihood From the Metropolis–Hastings Output
- Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models
- Bayesian analysis of switching ARCH models
- Bayesian Analysis of Nonlinear Autoregression Models Based on Neural Networks
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