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.
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
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.
LaplacesDemon function, stationarity is estimated
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.
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.
Statisticat, LLC. [email protected]
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.
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