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#' @title Initial values for HMC (Hamiltonian Moncte Carlo Markov Chains)
#'
#' @description An internal function.
#'
#'@details
#'This attempt failed, that is,
#'I cannot specify the initial values
#' so that the \code{rstan::sampling()} does not say the following:
#'
#'
#' Rejecting initial value:
#'
#'
#' Log probability evaluates to log(0), i.e. negative infinity.
#'
#'
#' Stan can't start sampling from this initial value.
#'
#'
#'
#'
#'
# @param dataList
#'@inheritParams fit_Bayesian_FROC
#' @return Initial values specification. See the detailed documentation for the init argument in \code{stan()}.
#' @export
#'
#' @examples
#'
#' init <- initial_values_specification_for_stan_in_case_of_MRMC(dataList.Chakra.Web)
#'
#'
#' # Where init is the variable of the rstan::stan() or rstan::sampling()
#'
#'
#'
#'
initial_values_specification_for_stan_in_case_of_MRMC <- function(dataList){
C <- dataList$C
M <- dataList$M
Q <- dataList$Q
dz <- vector()
mu <-array()
hyper_v <-vector()
A <- vector()
dz <- rep(1,C-1)
w <- 0.5
mu <- as.array(matrix(1, nrow = 3, ncol = 2))
v <- as.array(matrix(1, nrow = 3, ncol = 2))
hyper_v <- rep(0.5,Q)
A <- rep(0.5,M)
# real w;
# real <lower =0 > dz[C-1];
# real mu[M,Q];
# real <lower=0> v[M,Q];
# real <lower=0> hyper_v[Q];
#
# real <lower=0,upper=1>A[M];
return(list(
dz = dz ,
w =w,
mu =mu ,
v = v ,
hyper_v =hyper_v ,
A = A
))
}
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