Samples the degrees of freedom parameter of standardized and homoskedastic t-distributed input variates. Marginal data augmentation (MDA) is applied, tau is the vector of auxiliary latent states. Depending on the prior specification, nu might not be updated, just tau.
update_t_error(
homosked_data,
tau,
mean,
sd,
nu,
prior_spec,
do_tau_acceptance_rejection = TRUE
)de-meaned and homoskedastic observations
the vector of the latent states used in MDA. Updated in place
the vector of the conditional means // TODO update docs in R
the vector of the conditional standard deviations
parameter nu. The degrees of freedom for the t-distribution. Updated in place
prior specification object. See type_definitions.h
boolean. If TRUE, there is a correction for non-zero mean and non-unit sd, otherwise the proposal distribution is used
The function samples tau and nu from the following hierarchical model: homosked_data_i = sqrt(tau_i) * (mean_i + sd_i * N(0, 1)) tau_i ~ InvGamma(.5*nu, .5*(nu-2)) Naming: The data is homoskedastic ex ante in the model, mean_i and sd_i are conditional on some other parameter in the model. The prior on tau corresponds to a standardized t-distributed heavy tail on the data.
Other stochvol_cpp:
update_fast_sv(),
update_general_sv(),
update_regressors()