#'@rdname get_model_iid_gaussian_unknown_variance
#'@title get_model_iid_gaussian_unknown_variance
#'@description Univariate iid Gaussian observations with unknown variance (degenerate linear gaussian SSM)
#'@export
get_model_iid_gaussian_unknown_variance <- function(nu0, sigma02){
model = list()
model$observation_type = 'continuous'
# dimension of parameter
model$dimtheta = 1
model$dimY = 1
# fix some known parameters
model$mu = 0
# sampler from the prior distribution on parameters
model$rprior = function(Ntheta){
return (rbind(rinvchisq(Ntheta,nu0,sigma02)))
}
# prior distribution density on parameters
model$dprior = function(theta, log = TRUE){
return (dinvchisq(theta,nu0,sigma02,log))
}
# one-step predicitve density of the observation at time t given all the past from 1 to (t-1)
model$dpredictive = function(observations,t,theta,log = TRUE){
return (dnorm(observations[,t],mean = model$mu,sd = sqrt(theta), log))
}
# OPTIONAL: derivatives of the predicitve density with respect to the observation at time t
# inputs: observations (dimY by T matrix, with T >= t), time index t (int), theta (single vector),
# byproduct (OPTIONAL: auxiliary object needed to compute likelihood, e.g. Kalman filter)
# outputs: list with the following fields
# jacobian >> the transpose of the gradient (1 by dimY)
# hessiandiag >> the Hessian diagonal coefficients (1 by dimY)
# NB: if missing, this field is automatically filled with numerical derivatives
# via set_default_model in util_default.R)
model$derivativelogdpredictive = function(observations,t,theta,byproduct) {
deriv1 <- -(observations[,t]-model$mu)/theta
deriv2 <- -1/theta
return (list(jacobian = matrix(deriv1, 1, 1), hessiandiag = matrix(deriv2, 1, 1)))
}
# OPTIONAL: simulate Ny draws of y_t given theta and the past y_1 to y_(t-1)
# (with the convention y_0 = NULL)
# outputs: matrix of Ny draws of Yt given theta and past (dimY by Ny matrix)
model$rpredictive = function(Ny, t,theta,y_past){
return (matrix(rnorm(Ny, mean = model$mu, sd = sqrt(theta)),ncol = Ny))
}
return(model)
}
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