bayesControls function

Control parameters for ic_bayes

bayesControls(
  samples = 4000,
  chains = 4,
  useMLE_start = TRUE,
  burnIn = 2000,
  samplesPerUpdate = 1000,
  initSD = 0.1,
  updateChol = TRUE,
  acceptRate = 0.25,
  thin = 5
)

Arguments

• samples: Number of samples.
• chains: Number of MCMC chains to run
• useMLE_start: Should MLE used for starting point?
• burnIn: Number of samples discarded for burn in
• samplesPerUpdate: Number of iterations between updates of proposal covariance matrix
• initSD: If useMLE_start == FALSE, initial standard deviation used
• updateChol: Should cholesky decomposition be updated?
• acceptRate: Target acceptance rate
• thin: Amount of thinning

Details

Control parameters for the MH block updater used by ic_bayes.

The samples argument dictates how many MCMC samples are taken. One sample will be saved every thin iterations, so there will a total of thin * samples + burnIn iterations. The burn in samples are not saved at all.

Default behavior is to first calculate the MLE (not the MAP) estimate and use Hessian at the MLE to seed the proposal covariance matrix. After this, an updative covariance matrix is used. In cases with weakly informative likelihoods, using the MLE startpoint may lead to overly diffuse proposal or even undefined starting values. In this case, it suggested to use a cold start by setting useMLE_start = F

for the controls argument. In this case, the initial starting proposal covariance matrix will be a diagonal matrix with initSD standard deviations.