Flexible Bayesian Modeling – FBM

Radford Neal has contributed much over the years to Bayesian regression and classification theory and the areas of neural networks and machine learning. His FBM C based software routines provide modern methods in these areas and more. This software supports Bayesian regression and classification models based on neural networks and Gaussian processes, and Bayesian density estimation and clustering using mixture models and Dirichlet diffusion trees. It also supports a variety of Markov chain sampling methods, which may be applied to distributions specified by simple formulas, including simple Bayesian models defined by formulas for the prior and likelihood. For additional information check: http://www.cs.toronto.edu/~radford/fbm.software.html

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