ggdmcPrior (version 0.2.9.0)

sumlogprior: Sum of Log Prior Densities for a Joint Prior Distribution

Description

Computes the sum of log-densities for a vector of parameters, based on their respective prior distribution specifications. This function is used in Bayesian computations where the joint prior is the product of independent priors—thus, the log of the joint prior is the sum of log-densities.

Usage

sumlogprior(p_prior_r, parameters_r)

Value

A single numeric value: the sum of log-densities evaluated at the given parameter vector.

Arguments

p_prior_r

A list of prior specifications. Each element is itself a list specifying the prior for one parameter, typically created by BuildPrior. Each sublist should contain:

• p0: First parameter of the distribution (e.g., mean).

• p1: Second parameter (e.g., standard deviation or shape).

• lower: Lower bound for the parameter (if applicable).

• upper: Upper bound (if applicable).

• dist: Character string specifying the distribution type.

• log_p: Logical; should be TRUE for log-density computation.

parameters_r

A numeric vector of parameter values at which to evaluate the prior. Must be the same length as p_prior_r.

Details

This function performs the following steps:

• Iterates over each parameter and its associated prior specification

• Evaluates the log-density for each parameter

• Sums all log-densities to compute the joint log prior

This is useful, for example, in computing the log-posterior of hierarchical Bayesian models where priors are assumed to be independent.

Examples

p0 <- c(A = 0.15, B = 0.45, mean_v = 2.25, sd_v = 0.15, t0 = 0.2)
tnorm_prior <- BuildPrior(
    p0 = p0,
    p1 = rep(1, 5),
    lower = rep(0, 5),
    upper = rep(NA, 5),
    dist = rep("tnorm", 5),
    log_p = rep(TRUE, 5)
)

npar <- length(tnorm_prior)
theta <- runif(npar, 0, 10)
result <- sumlogprior(p_prior_r = tnorm_prior, parameters_r = theta)
print(result)