Debiasing MCMC with couplings

continue_coupled_chains | Continue coupled MCMC chains up to m steps |

coupled_chains | Coupled MCMC chains |

debiasedmcmc-package | debiasedmcmc |

digamma | compute log-density of inverse gamma |

dinvgaussian | compute log-density of inverse Gaussian |

expit | expit |

fast_dmvnorm | fast_dmvnorm |

fast_rmvnorm | fast_rmvnorm |

gaussian_max_coupling | Maximal coupling of two multivariate Normal distributions |

gaussian_max_coupling_cholesky_R | Maximal coupling of two multivariate Normal distributions |

gaussian_opt_transport | Optimal transport coupling between two multivariate Normals |

get_blasso | Y and X need to be matrices, and lambda non-negative |

get_max_coupling | Sample from maximally coupled distributions p and q |

get_mh_kernel | Get random walk Metropolis-Hastings kernels |

get_variableselection | Y and X need to be matrices, and lambda non-negative |

H_bar | Compute unbiased estimators from coupled chains |

hello | Hello, World! |

histogram_c_chains | histogram_c_chains |

logistic_precomputation | Precomputation to prepare for the Polya-Gamma sampler |

pg_gibbs | Polya-Gamma Gibbs sampler |

rcpp_hello | Hello, Rcpp! |

rgamma_coupled | Sample from maximally coupled Gamma |

rigamma | Sample from inverse gamma |

rigamma_coupled | Sample from maximally coupled inverse gamma |

rinvgaussian_coupled | Sample from maximally coupled inverse Gaussian |

rnorm_max_coupling | Maximal coupling of two univariate Normal distributions |

setmytheme | Customize graphical settings |

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