kldiv: Estimate the Kullback-Leibler divergence between two random...
In cbrown5/BayeSens: Prior sensitivity analysis for Bayesian MCMC models

Description Usage Arguments Details Value Author(s)

View source: R/kldiv.R

Estimate the Kullback-Leibler divergence between two random variates

1 2	kldiv(x1, x2, nbreaks = 100, minx = min(c(x1, x2)), maxx = max(c(x1, x2)), small = 0.01)

`x1`	A `numeric` random variate of draws from the posterior distribution
`x2`	A `numeric` random variate of draws from the prior distribution
`nbreaks`	A single `numeric` giving how many breaks to break the discrete distribution into
`minx`	A single `numeric` giving the lower bound of integration
`maxx`	A single `numeric` giving the upper bound of integration
`small`	small number added to histogram counts to prevent division by zero

Kullback-Leibler divergence is approximated by binning the random variates and calculating the KL-div for discrete distributions.

It is recommended to visually check distribution fits, particularly if the number of random variates is small.

In general this methods will be inaccurate if analysis is performed on too few samples, e.g. <10 000. >100 000 would be ideal.

A helldist object containing approximate Hellinger distances and fitted density kernals. hdist_discEstimate of Hellinger distance using discrete approximation of the distributions hdist_contEstimate of Hellinger distance using continous approximation of distributions

Christopher J. Brown christo.j.brown@gmail.com

cbrown5/BayeSens documentation built on April 26, 2020, 12:40 a.m.