DEMCMC: DEMCMC

In DEBBI: Differential Evolution-Based Bayesian Inference

DEMCMC

Description

Sample from posterior using Differential Evolution Markov Chain Monte Carlo

Usage

DEMCMC(LogPostLike, control_params = AlgoParamsDEMCMC(), ...)

Arguments

LogPostLike	function whose first argument is an n_params-dimensional model parameter vector and returns (scalar) sum of log prior density and log likelihood for the parameter vector.
control_params	control parameters for DEMCMC algorithm. see AlgoParamsDEMCMC function documentation for more details. You must specify 'n_params' here.
...	additional arguments to pass LogPostLike

Value

list contain posterior samples from DEMCMC in a 'n_samples_per_chain' by 'n_chains' by n_params array and the log posterior likelihood of each sample in a 'n_samples_per_chain' by 'n_chains' array.

Examples

# simulate from model
dataExample <- matrix(stats::rnorm(100, c(-1, 1), c(1, 1)), nrow = 50, ncol = 2, byrow = TRUE)
#
# list parameter names
param_names_example <- c("mu_1", "mu_2")

# log posterior likelihood function = log likelihood + log prior | returns a scalar
LogPostLikeExample <- function(x, data, param_names) {
  out <- 0

  names(x) <- param_names

  # log prior
  out <- out + sum(dnorm(x["mu_1"], 0, sd = 1, log = TRUE))
  out <- out + sum(dnorm(x["mu_2"], 0, sd = 1, log = TRUE))

  # log likelihoods
  out <- out + sum(dnorm(data[, 1], x["mu_1"], sd = 1, log = TRUE))
  out <- out + sum(dnorm(data[, 2], x["mu_2"], sd = 1, log = TRUE))

  return(out)
}

# Sample from posterior
DEMCMC(
  LogPostLike = LogPostLikeExample,
  control_params = AlgoParamsDEMCMC(
    n_params = length(param_names_example),
    n_iter = 1000,
    n_chains = 12
  ),
  data = dataExample,
  param_names = param_names_example
)

DEBBI documentation built on May 17, 2022, 5:08 p.m.