Andrieu C, Doucet A, de_Freitas N. (2000). Robust full Bayesian methods for neural networks. Advances in neural information processing systems. 12

Bishop C. (1995). Neural Networks For Pattern Recognition.

Bollerslev T. (1986). A generalized autoregressive conditional heteroskedasticity J Economet. 31

Carlin B, Louis T. (1996). Bayes and empirical Bayes methods for data analysis.

Chen M, Shao Q, Ibrahim J. (2000). Monte Carlo methods in Bayesian computation.

Chib S. (1995). Marginal likelihood from the Gibbs output J Am Stat Assoc. 90

Chib S, Greenberg E. (1995). Understanding the Metropolis-Hastings algorithm Am Statistician. 49

Chib S, Greenberg E. (1996). Markov chain Monte Carlo simulation methods in econometrics Econometric Theory. 12

Chib S, Jeliazkov I. (2001). Marginal likelihood from the Metropolis-Hastings output J Am Stat Assoc. 96

Donaldson R, Kamstra M. (1997). An artificial neural network-GARCH model for international stock return volatility J Empirical Finance. 4

Dorffner G, Miazhynskaia T. (2006). A comparison of Bayesian model selection based on MCMC with an application to GARCH-type models Statistical Papers. 47

Dorffner G, Schittenkopf C, Dockner EJ. (2000). Forecasting time-dependent conditional densities: A seminonparametric neural network approach J Forecasting. 19

Dunis CL, Jalilov J. (2002). Neural network regression and alternative forecasting techniques for predicting financial variables Neural Network World. 12

Fruhwirth-Schnatter S. (1995). Bayesian model discrimination and Bayes factor for linear gaussian state space models J R Stat Soc Series B. 57

Fruhwirth-Schnatter S. (2004). Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques Econometrics J. 7

Fruhwirth-Schnatter S, Kaufmann S. (2002). Bayesian analysis of switching ARCH models J Time Series Anal. 23

Fruhwirth-schnatter S. (2001). MCMC estimation of classical and dynamic switching and mixture models J Am Stat Assoc. 96

Gelfand A, Dey D. (1994). Bayesian model choice: Asymptotic and exact calculations J R Stat Soc Series B. 56

Geweke J. (1989). Exact predictive densities for linear models with ARCH disturbances J Econometrics. 40

Geweke J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments Bayesian Statistics. 4

Geweke J. (1999). Using simulation methods for Bayesian econometric models: Inference, development and communication Econometric Rev. 18

Green P. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination Biometrika. 82

Holmes CC, Mallick BK. (1998). Bayesian radial basis functions of variable dimension Neural Comput. 10

Huang X, Dunis C. (2001). Forecasting and trading currency volatility: An application of recurrent neural regression and model combination Tech Rep Liverpool Business School.

Lampinen J, Vehtari A. (2001). Bayesian approach for neural networks--review and case studies. Neural networks : the official journal of the International Neural Network Society. 14 [PubMed]

Lee H. (1999). Model selection and model averaging for neural networks Unpublished doctoral dissertation, Carnegie Mellon University.

Lewis S, Raftery A. (1992). How many iterations in the Gibbs sampler Bayesian Statistics. 4

Locarek-Junge H, Prinzler R. (1998). Estimating value-at-risk using neural networks Information systeme in der Finanzwirtschaft.

Mackay DJC. (1992). A practical Bayesian framework for back propagation networks Neural Comput. 4

Marrs A. (1998). An application of reversible-jump MCMC to multivariate spherical gaussian mixtures Advances In Neural Information Processing Systems. 10

Menchero A, Diez RM, Insua DR, Muller P. (2005). Bayesian Analysis of Nonlinear Autoregression Models Based on Neural Networks Neural Comput. 17

Meng XL, Wong WA. (1996). Simulating ratios of normalizing constantsvia a simple identity: A theoretical exploration Statistica Sinica. 6

Müller P, Insua DR. (1998). Issues in Bayesian Analysis of Neural Network Models Neural computation. 10 [PubMed]

Nakatsuma T. (2000). Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach J Econometrics. 95

Neal RM. (1996). Bayesian learning for neural networks.

Raftery A, Newton M. (1994). Approximate Bayesian inference by the weighted likelihood bootstrap J R Stat Soc Series B. 56

Richardson S, Gilks WR, Spiegelhalter DJ. (1996). Markov chain Monte Carlo in practice.

Smith A, Hills S. (1992). Parameterization issues in Bayesian inference Bayesian Statistics. 4

Stephens M. (2000). Dealing with label switching in mixture models J R Stat Soc Series B. 62

Vehtari A, Lampinen J. (2002). Bayesian model assessment and comparison using cross-validation predictive densities. Neural computation. 14 [PubMed]

Yao J, Tan C. (2001). Guidelines for financial forecasting with neural networks Proc Intl Conf Neural Inform Process.

Zhong M, Darrat AF. (2000). On testing the random walk hypothesis: A model comparison approach Financial Rev. 35