IAT function estimates integrated autocorrelation time,
which is the computational inefficiency of a continuous chain or MCMC
sampler. IAT is also called the IACT, ACT, autocorrelation time,
autocovariance time, correlation time, or inefficiency factor. A lower
IAT is better.
IAT is a MCMC diagnostic that is
an estimate of the number of iterations, on average, for an
independent sample to be drawn, given a continuous chain or Markov
chain. Put another way,
IAT is the number of correlated samples
with the same variance-reducing power as one independent sample.
IAT is a univariate function. A multivariate form is not included.
This requried argument is a vector of samples from a chain.
IAT is a MCMC diagnostic that is often used to compare
continuous chains of MCMC samplers for computational inefficiency,
where the sampler with the lowest
IATs is the most efficient
sampler. Otherwise, chains may be compared within a model, such as
with the output of
LaplacesDemon to learn about the
inefficiency of the continuous chain. For more information on
comparing MCMC algorithmic inefficiency, see the
IAT is also estimated in the
IAT is usually applied to a stationary, continuous
chain after discarding burn-in iterations (see
for more information). The
IAT of a continuous chain correlates
with the variability of the mean of the chain, and relates to
Effective Sample Size (
ESS) and Monte Carlo Standard
ESS are inversely related, though not
perfectly, because each is estimated a little differently. Given
N samples and taking autocorrelation into account,
ESS estimates a reduced number of M samples.
IAT estimates the number of autocorrelated samples,
on average, required to produce one independently drawn sample.
IAT function is similar to the
IAT function in the
Rtwalk package of Christen and Fox (2010), which is currently
unavailabe on CRAN.
IAT function returns the integrated autocorrelation time of
Statisticat, LLC. firstname.lastname@example.org
Christen, J.A. and Fox, C. (2010). "A General Purpose Sampling Algorithm for Continuous Distributions (the t-walk)". Bayesian Analysis, 5(2), p. 263–282.
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