The burnin function estimates the duration of burn-in in
iterations for one or more Markov chains. ``Burn-in'' refers to the
initial portion of a Markov chain that is not stationary and is still
affected by its initial value.
burnin(x, method="BMK")This is a vector or matrix of posterior samples for which a the number of burn-in iterations will be estimated.
This argument defaults to "BMK", in which case
stationarity is estimated with the BMK.Diagnostic
function. Alternatively, the Geweke.Diagnostic
function may be used when method="Geweke" or the
KS.Diagnostic function may be used when
method="KS".
The burnin function returns a vector equal in length to the
number of MCMC chains in x, and each element indicates the
maximum iteration in burn-in.
Burn-in is a colloquial term for the initial iterations in a Markov chain prior to its convergence to the target distribution. During burn-in, the chain is not considered to have ``forgotten'' its initial value.
Burn-in is not a theoretical part of MCMC, but its use is the norm because of the need to limit the number of posterior samples due to computer memory. If burn-in were retained rather than discarded, then more posterior samples would have to be retained. If a Markov chain starts anywhere close to the center of its target distribution, then burn-in iterations do not need to be discarded.
In the LaplacesDemon function, stationarity is estimated
with the BMK.Diagnostic function on all thinned
posterior samples of each chain, beginning at cumulative 10% intervals
relative to the total number of samples, and the lowest number in
which all chains are stationary is considered the burn-in.
The term, ``burn-in'', originated in electronics regarding the initial testing of component failure at the factory to eliminate initial failures (Geyer, 2011). Although ``burn-in' has been the standard term for decades, some are referring to these as ``warm-up'' iterations.
Geyer, C.J. (2011). "Introduction to Markov Chain Monte Carlo". In S Brooks, A Gelman, G Jones, and M Xiao-Li (eds.), "Handbook of Markov Chain Monte Carlo", p. 3--48. Chapman and Hall, Boca Raton, FL.
BMK.Diagnostic,
deburn,
Geweke.Diagnostic,
KS.Diagnostic, and
LaplacesDemon.
# NOT RUN {
library(LaplacesDemon)
x <- rnorm(1000)
burnin(x)
# }
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