bayesControls {icenReg}

Control parameters for ic_bayes

Description

Control parameters for ic_bayes

Usage

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.