likelihood.seqtoone_normal {BayesFluxR}  R Documentation 
This creates a likelihood of the form
y_i \sim Normal(net(x_i), \sigma), i=1,...,N
Here x_i
is a subsequence which will be fed through the recurrent
network to obtain the final output net(x_i) = \hat{y}_i
. Thus, if
one has a single time series, and splits the single time series into subsequences
of length K which are then used to predict the next output of the time series, then
each x_i
consists of K consecutive observations of the time series. In a sense
one constraints the maximum memory length of the network this way.
likelihood.seqtoone_normal(chain, sig_prior)
chain 
Network structure obtained using 
sig_prior 
A prior distribution for sigma defined using

see likelihood.feedforward_normal
## Not run:
## Needs previous call to `BayesFluxR_setup` which is time
## consuming and requires Julia and BayesFlux.jl
BayesFluxR_setup(installJulia=TRUE, seed=123)
net < Chain(RNN(5, 1))
like < likelihood.seqtoone_normal(net, Gamma(2.0, 0.5))
prior < prior.gaussian(net, 0.5)
init < initialise.allsame(Normal(0, 0.5), like, prior)
x < array(rnorm(5*100*10), dim=c(10,5,100))
y < rnorm(100)
bnn < BNN(x, y, like, prior, init)
BNN.totparams(bnn)
## End(Not run)