plot.KbMCMC {EntropyMCMC}

Plot sequences of estimates of Kullback distance or Entropy against iterations

Description

This S3 method for plot plots by default sequences of estimates of the Kullback distance K(p^t,f) between the (estimated) pdf of the MCMC algorithm at time t, p^t, and the target density f, for t=1 up to the number of iterations that have been provided/computed. It can also plot the first term in the Kullback distance, i.e. the Entropy E_{p^t}[\log(p^t)]. Its argument is an object of class KbMCMC such as the one returned by, e.g., EntropyMCMC.

Usage

## S3 method for class 'KbMCMC'
plot(x, Kullback = TRUE, lim = NULL, ylim = NULL, 
            new.plot = TRUE, title = NULL, ...)

Arguments

x

An object of class KbMCMC, such as the one returned by EntropyMCMC.

Kullback

TRUE to plot the Kullback distance, FALSE to plot the Entropy.

lim

for zooming over 1:lim iterations only.

ylim

y limits, passed to plot.

new.plot

set to FALSE to add the plot to an existing plot.

title

The title; if NULL, then a default title is displayed.

...

Further parameters passed to plot or lines.

Value

The graphic to plot.

Author(s)

Didier Chauveau.

References

• Chauveau, D. and Vandekerkhove, P. (2012), Smoothness of Metropolis-Hastings algorithm and application to entropy estimation. ESAIM: Probability and Statistics, 17, (2013) 419–431. DOI: http://dx.doi.org/10.1051/ps/2012004

• Chauveau D. and Vandekerkhove, P. (2014), Simulation Based Nearest Neighbor Entropy Estimation for (Adaptive) MCMC Evaluation, In JSM Proceedings, Statistical Computing Section. Alexandria, VA: American Statistical Association. 2816–2827.

• Chauveau D. and Vandekerkhove, P. (2014), The Nearest Neighbor entropy estimate: an adequate tool for adaptive MCMC evaluation. Preprint HAL http://hal.archives-ouvertes.fr/hal-01068081.

See Also

EntropyMCMC, EntropyMCMC.mc

Examples

## See the EntropyMCMC Examples.