Daniel@0: Daniel@0:
Daniel@0:Daniel@0: demhmc3Daniel@0: Daniel@0: Daniel@0:
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 (with persistence) is used to sample from the posterior
Daniel@0: distribution of the weights. The graph shows the underlying function,
Daniel@0: 300 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: demhmc2
, hmc
, mlp
, mlperr
, mlpgrad
Copyright (c) Ian T Nabney (1996-9) Daniel@0: Daniel@0: Daniel@0: Daniel@0: