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##' prior specification
##'
##' Specification of prior distributions via the rprior and dprior components.
##'
##' @name prior_spec
##' @aliases priors
##' @rdname prior_spec
##' @family implementation information
##' @family Bayesian methods
##' @seealso \code{\link{dprior}} \code{\link{rprior}}
##' @inheritSection pomp Note for Windows users
##' @details
##' A prior distribution on parameters is specified by means of the \code{rprior} and/or \code{dprior} arguments to \code{pomp}.
##' As with the other \link[=basic_components]{basic model components}, it is preferable to specify these using C snippets.
##' In writing a C snippet for the prior sampler (\code{rprior}), keep in mind that:
##' \enumerate{
##' \item Within the context in which the snippet will be evaluated, only the parameters will be defined.
##' \item The goal of such a snippet is the replacement of parameters with values drawn from the prior distribution.
##' \item Hyperparameters can be included in the ordinary parameter list.
##' Obviously, hyperparameters should not be replaced with random draws.
##' }
##' In writing a C snippet for the prior density function (\code{dprior}), observe that:
##' \enumerate{
##' \item Within the context in which the snippet will be evaluated, only the parameters and \code{give_log} will be defined.
##' \item The goal of such a snippet is computation of the prior probability density, or the log of same, at a given point in parameter space.
##' This scalar value should be returned in the variable \code{lik}.
##' When \code{give_log == 1}, \code{lik} should contain the log of the prior probability density.
##' \item Hyperparameters can be included in the ordinary parameter list.
##' }
##' \link[=Csnippet]{General rules for writing C snippets can be found here}.
##'
##' Alternatively, one can furnish \R functions for one or both of these arguments.
##' In this case, \code{rprior} must be a function that makes a draw from
##' the prior distribution of the parameters and returns a named vector
##' containing all the parameters.
##' The only required argument of this function is \code{...}.
##'
##' Similarly, the \code{dprior} function must evaluate the prior probability
##' density (or log density if \code{log == TRUE}) and return that single
##' scalar value.
##' The only required arguments of this function are \code{...} and \code{log}.
##' @section Default behavior:
##' By default, the prior is assumed flat and improper.
##' In particular, \code{dprior} returns \code{1} (\code{0} if \code{log = TRUE}) for every parameter set.
##' Since it is impossible to simulate from a flat improper prior, \code{rprocess} returns missing values (\code{NA}s).
##'
##' @example examples/prior_spec.R
##'
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