wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual demhmc3 wolffd@0: wolffd@0: wolffd@0: wolffd@0:

demhmc3 wolffd@0:

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wolffd@0: Purpose wolffd@0:

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

wolffd@0: Synopsis wolffd@0:

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wolffd@0: demhmc3
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wolffd@0: Description wolffd@0:

wolffd@0: The problem consists of one input variable x and one target variable wolffd@0: t with data generated by sampling x at equal intervals and then wolffd@0: generating target data by computing sin(2*pi*x) and adding Gaussian wolffd@0: noise. The model is a 2-layer network with linear outputs, and the hybrid Monte wolffd@0: Carlo algorithm (with persistence) is used to sample from the posterior wolffd@0: distribution of the weights. The graph shows the underlying function, wolffd@0: 300 samples from the function given by the posterior distribution of the wolffd@0: weights, and the average prediction (weighted by the posterior probabilities). wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: demhmc2, hmc, mlp, mlperr, mlpgrad
wolffd@0: Pages: wolffd@0: Index wolffd@0:
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Copyright (c) Ian T Nabney (1996-9) wolffd@0: wolffd@0: wolffd@0: wolffd@0: