Sequential Monte Carlo for model selection and estimation of neural networks
We address the complex problem of sequential Bayesian learning and model selection for neural networks. This problem does not usually admit any type of closed-form analytical solution and, as a result, one has to resort to numerical methods. We propose here an original sequential simulation-based st...
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| Format: | Conference item |
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2000
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