PsMCMC: Pseudo-Marginal MCMC

In JMacDonaldPhD/REpi: Pseudo-Marginal Analysis of partially observed Epidemics

Pseudo-Marginal MCMC

Description

Generates dependent samples from target distribution using a Pseudo-Marginal MCMC proposal scheme.

Usage

PsMCMC(
  init,
  epiModel,
  obsFrame,
  epiSample,
  I0,
  alpha,
  logPrior,
  lambda,
  V,
  N,
  noIts
)

Arguments

init	initial value of epidemic parameters
epiModel	epidemic model
obsFrame	Generator function for observational model.
epiSample	Observed epidemic data.
I0	Initial state of the epidemic, which assumed to be known.
alpha	Observational parameters to be passed on to the log-likelihood function generated by obsFrame(X_sim)
logPrior	Functions which calculate log-density of the assumed priors of the epidemic parameters.
lambda	Random Walk Metropolis (RWM) proposal scale parameter
V	A square matrix containing the covariance values for RWM proposal. Shape of matrix should match the length of the init parameter.
N	Number of particles to be used in Importance Sample Estimate of posterior. This can be optimised using adapt_IS()
noIts	Number of iterations of MCMC sampler scheme to carry out.

Value

A matrix of MCMC samples and proposal acceptance rate of the sample.

JMacDonaldPhD/REpi documentation built on Aug. 2, 2022, 2:09 p.m.