Bayesian methods for adaptive models
Bayesian methods for adaptive models
Neural networks in applied statistics
Technometrics
Neural network models for time series forecasts
Management Science
Issues in Bayesian analysis of neural network models
Neural Computation
Bayesian radial basis functions of variable dimension
Neural Computation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Neural network models for conditional distribution under bayesian analysis
Neural Computation
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We show how Bayesian neural networks can be used for time-series analysis. We consider a block-based model building strategy to model linear and nonlinear features within the time series: a linear combination of a linear autoregression term and a feedforward neural network (FFNN) with an unknown number of hidden nodes. To allow for simpler models, we also consider these terms separately as competing models to select from. Model identifiability problems arise when FFNN sigmoidal activation functions exhibit almost linear behavior or when there are almost duplicate or irrelevant neural network nodes. New reversible-jump moves are proposed to facilitate model selection, mitigating model identifiability problems. We illustrate this methodology analyzing several time-series data examples.