plot.KbMCMC {EntropyMCMC}  R Documentation 
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
.
## S3 method for class 'KbMCMC'
plot(x, Kullback = TRUE, lim = NULL, ylim = NULL,
new.plot = TRUE, title = NULL, ...)
x 
An object of class 
Kullback 

lim 
for zooming over 
ylim 

new.plot 
set to 
title 
The title; if 
... 
Further parameters passed to 
The graphic to plot.
Didier Chauveau.
Chauveau, D. and Vandekerkhove, P. (2012), Smoothness of MetropolisHastings 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.archivesouvertes.fr/hal01068081.
## See the EntropyMCMC Examples.