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Erschienen in:
1996 | OriginalPaper | Buchkapitel
Conclusions and Further Work
verfasst von : Radford M. Neal
Erschienen in: Bayesian Learning for Neural Networks
Verlag: Springer New York
Enthalten in: Professional Book Archive
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The preceding three chapters have examined the meaning of Bayesian neural network models, showed how these models can be implemented by Markov chain Monte Carlo methods, and demonstrated that such an implementation can be applied in practice to problems of moderate size, with good results. In this concluding chapter, I will review what has been accomplished in these areas, and describe on-going and potential future work to extend these results, both for neural networks and for other flexible Bayesian models.