Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual demhmc2 Daniel@0: Daniel@0: Daniel@0: Daniel@0:

demhmc2 Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

Daniel@0: 
Daniel@0: demhmc2
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Daniel@0: Description Daniel@0:

Daniel@0: The problem consists of one input variable x and one target variable Daniel@0: t with data generated by sampling x at equal intervals and then Daniel@0: generating target data by computing sin(2*pi*x) and adding Gaussian Daniel@0: noise. The model is a 2-layer network with linear outputs, and the hybrid Monte Daniel@0: Carlo algorithm (without persistence) is used to sample from the posterior Daniel@0: distribution of the weights. The graph shows the underlying function, Daniel@0: 100 samples from the function given by the posterior distribution of the Daniel@0: weights, and the average prediction (weighted by the posterior probabilities). Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: demhmc3, hmc, mlp, mlperr, mlpgrad
Daniel@0: Pages: Daniel@0: Index Daniel@0:
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Copyright (c) Ian T Nabney (1996-9) Daniel@0: Daniel@0: Daniel@0: Daniel@0: