Compute Highest Posterior Density Intervals
Compute the Highest Posterior Density Interval (HPD) from an inverse density function (hpd) or a vector of realizations of the distribution (emp.hpd).
hpd(posterior.icdf, conf=0.95, tol=0.00000001,...) emp.hpd(x, conf=0.95)
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 538280@gmail.com
hdr in the hdrcde package.
hpd(qbeta, shape1=50, shape2=250) tmp <- rbeta(10000, 50, 250) emp.hpd(tmp)
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