adaptiveSIR_PseudoMarginalMCMC: Adaptive Pseudo-Marginal MCMC for SIR Epidemic Panel Data
In JMacDonaldPhD/Epidemics: Inference For Epidemics (one line, title case)

Description Usage Arguments Value See Also

Adapts proposal parameters for with a view of optimal of a target using Pseudo-Marginal MCMC scheme.

adaptiveSIR_PseudoMarginalMCMC(
  obsTransData,
  I_0,
  obsTimes,
  N,
  beta0,
  gamma0,
  lambda0 = 2.38/sqrt(2),
  V0 = diag(c(1/N, 1)),
  delta = 0.05,
  noSims,
  noIts,
  burnIn,
  thinningFactor = 1,
  lagMax = NA,
  parallel = FALSE,
  noCores
)

`obsTransData`	Interpanel transition data.
`I_0`	Initial number of infectives in the population.
`obsTimes`	Times at which epidemic cohort were followed up.
`N`	Population size.
`beta0`	Starting value for infectious process parameter.
`gamma0`	Starting value for removal/recovery process parameter.
`lambda0`	Starting value for RWM proposal parameter which is to be adapted.
`V0`	Starting state for RWM proposal Covariance matrix which is to be adapted.
`delta`	Probability that a proposal is made based on the starting proposal conditions.
`noSims`	Number of epidemic simulations to be used per iteration for estimation of likelihood.
`noIts`	Number of MCMC iterations.
`thinningFactor`	Controls the factor by which MCMC samples are thinned, to reduce dependency.
`lagMax`	Plotting parameter for acf() function.
`parallel`	Are epidemic simulations run in parallel?
`noCores`	If epidemic simulations are run in parallel, this is the number of cores utilised.