Specify the priors. Without inputs, defaults will be used.
priors.spec(m0 = 0, CS0 = 3, n0 = 0.001, d0 = 0.001)the value of the prior mean at time t=0, scalar (assumed to be the same
for all nodes). The default is zero.
controls the scaling of the prior variance matrix C*_{0} at time
t=0. The default is 3, giving a non-informative prior for C*_{0}, 3 x (p x p)
identity matrix. p is the number of thetas.
prior hyperparameter of precision phi ~ G(n_{0}/2; d_{0}/2). The default
is a non-informative prior, with n0 = d0 = 0.001. n0 has to be higher than 0.
prior hyperparameter of precision phi ~ G(n_{0}/2; d_{0}/2). The default
is a non-informative prior, with n0 = d0 = 0.001.
priors a list with the prior hyperparameters. Relevant to dlm.lpl,
exhaustive.search, node, subject.
At time t=0, (theta_{0} | D_{0}, phi) ~ N(m_{0},C*_{0} x phi^{-1}),
where D_{0} denotes the set of initial information.
West, M. & Harrison, J., 1997. Bayesian Forecasting and Dynamic Models. Springer New York.