Module Documentation#

class dime_sampler.moves.DIMEMove(sigma=1e-05, gamma=None, aimh_prob=0.1, df_proposal_dist=10, delta=0.999, **kwargs)#

A proposal using adaptive differential-independence mixture ensemble MCMC.

This is the Differential-Independence Mixture Ensemble proposal as developed in Ensemble MCMC Sampling for DSGE Models.

Parameters:

  • sigma (float, optional) – standard deviation of the Gaussian used to stretch the proposal vector.

  • gamma (float, optional) – mean stretch factor for the proposal vector. By default, it is \(2.38 / \sqrt{2\,\mathrm{ndim}}\) as recommended by ter Braak (2006).

  • aimh_prob (float, optional) – probability to draw an adaptive independence Metropolis Hastings (AIMH) proposal. By default this is set to \(0.1\).

  • df_proposal_dist (float, optional) – degrees of freedom of the multivariate t distribution used for AIMH proposals. Defaults to \(10\).

class dime_sampler.moves.IMHMove(mean, cov, df_proposal_dist=10, **kwargs)#

A proposal using independence MCMC.

This is a standard independence MCMC move using a multivariate Student distribution.

Parameters:

  • mean (array) – mean the proposal multivariate t distribution. Defaults to \(10\).

  • cov (array) – covariance of the proposal multivariate t distribution. Defaults to \(10\).

  • df_proposal_dist (float, optional) – degrees of freedom of the multivariate t distribution. Defaults to \(10\).