Conclusions and Further Work

Abstract

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.