Description Usage Arguments Details Value Note Author(s) See Also Examples

Compute the Highest Posterior Density Interval (HPD) from an inverse density function (hpd) or a vector of realizations of the distribution (emp.hpd).

1 2 3 |

`posterior.icdf` |
Function, the inverse cdf of the posterior distribution (usually a function whose name starts with 'q'). |

`x` |
A vector of realizations from the posterior distribution. |

`conf` |
Scalar, the confidence level desired. |

`tol` |
Scalar, the tolerance for |

`...` |
Additional arguments to |

These functions compute the highest posterior density intervals
(sometimes called minimum length confidence intervals) for a Bayesian
posterior distribution. The `hpd`

function is used when you have
a function representing the inverse cdf (the common case with
conjugate families). The `emp.hpd`

function is used when you
have realizations of the posterior (when you have results from an MCMC
run).

A vector of length 2 with the lower and upper limits of the interval.

These functions assume that the posterior distribution is unimodal, they compute only 1 interval, not the set of intervals that are appropriate for multimodal distributions.

Greg Snow [email protected]

`hdr`

in the hdrcde package.

1 2 3 4 |

TeachingDemos documentation built on May 29, 2017, 11:33 a.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.