MCMCcopies.mc {EntropyMCMC}  R Documentation 
Simulates nmc
iid copies of a MCMC algorithm mcmc_algo
for n
(time) iterations and returns an object of class plMCMC
(for parallel MCMC) holding
an array of the trajectories and running information.
This functions is similar to MCMCcopies
except that it uses the
parallel package (available in the main distribution, but not for Windows machines)
to split the task between the available
virtual cores on the computer.
MCMCcopies.mc(mcmc_algo, n = 100, nmc = 10, Ptheta0, target, f_param, q_param,
verb = TRUE, nbcores=detectCores())
mcmc_algo 
a list defining an MCMC algorithm in terms of the
functions it uses, such as 
n 
The number of (time) iterations of each single chain to run. 
nmc 
The number of iid copies of each single chain. 
Ptheta0 
A 
target 
The target density for which the MCMC algorithm is defined;
may be given only up to a multiplicative constant for most MCMC.
target must be a function such as the multidimensional gaussian

f_param 
A list holding all the necessary target parameters, consistent with the target definition. 
q_param 
A list holding all the necessary parameters
for the proposal density of the MCMC algorithm 
verb 
Verbose mode for summarizing output during the simulation. 
nbcores 
Number of required (virtual) cores, defaults to all as returned
by 
MCMCcopies.mc
, like MCMCcopies
, sequentially simulates
nmc
iid copies of the
MCMC algorithm passed in the list mcmc_algo
,
for n
(time) iterations, and returns an object of class plMCMC
holding an array of the trajectories and running information.
The list mcmc_algo
must contain the named elements:
name
, the name of the MCMC, such as "RWHM"
chain
, the function for simulation of n steps of a single chain
step
, the function for simulation of 1 step of that algorithm
q_pdf
, the density of the proposal
q_proposal
, the function that simulates a proposal
For examples, see the algorithms currently implemented:
RWHM
, the Random Walk HastingMetropolis with gaussian proposal;
HMIS_norm
, an Independence Sampler HM with gaussian proposal;
AMHaario
, the AdaptiveMetropolis (AM) from Haario (2001);
IID_norm
, a gaussian iid sampler which is merely
a "fake" MCMC for testing purposes.
MCMCcopies
returns a list of class plMCMC
with items:
Ptheta 
The 
prob.accept 
The estimated rate of acceptation over all simulations. 
algo 
The MCMC algorithm name i.e. 
target 
The target density. 
f_param 
The list holding all the target parameters. 
q_param 
The list holding all the proposal density parameters. 
Didier Chauveau.
Chauveau, D. and Vandekerkhove, P. (2013), Smoothness of MetropolisHastings algorithm and application to entropy estimation. ESAIM: Probability and Statistics, 17, 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.
A more general cluster version MCMCcopies.cl
,
a single core version MCMCcopies
,
and functions doing simulation and entropy and Kullback estimation simultaneously:
EntropyParallel
and EntropyParallel.cl
## Toy example using the bivariate gaussian target
## not working on Windows since socket cluster not implemented
n = 150; nmc = 20; d=2 # bivariate example
varq=0.1 # variance of the proposal (chosen too small)
q_param=list(mean=rep(0,d),v=varq*diag(d))
## initial distribution, located in (2,2), "far" from target center (0,0)
Ptheta0 < DrawInit(nmc, d, initpdf = "rnorm", mean = 2, sd = 1)
# simulations (may be compared with the singlecore version using system.time)
s1 < MCMCcopies.mc(RWHM, n, nmc, Ptheta0, target_norm,
target_norm_param, q_param, nbcores = 2)
summary(s1) # method for "plMCMC" object
## see MCMCcopies example for plots