11,821 research outputs found
Markov Chain Monte Carlo confidence intervals
For a reversible and ergodic Markov chain with invariant
distribution , we show that a valid confidence interval for can
be constructed whenever the asymptotic variance is finite and
positive. We do not impose any additional condition on the convergence rate of
the Markov chain. The confidence interval is derived using the so-called
fixed-b lag-window estimator of . We also derive a result that
suggests that the proposed confidence interval procedure converges faster than
classical confidence interval procedures based on the Gaussian distribution and
standard central limit theorems for Markov chains.Comment: Published at http://dx.doi.org/10.3150/15-BEJ712 in the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Relative fixed-width stopping rules for Markov chain Monte Carlo simulations
Markov chain Monte Carlo (MCMC) simulations are commonly employed for
estimating features of a target distribution, particularly for Bayesian
inference. A fundamental challenge is determining when these simulations should
stop. We consider a sequential stopping rule that terminates the simulation
when the width of a confidence interval is sufficiently small relative to the
size of the target parameter. Specifically, we propose relative magnitude and
relative standard deviation stopping rules in the context of MCMC. In each
setting, we develop sufficient conditions for asymptotic validity, that is
conditions to ensure the simulation will terminate with probability one and the
resulting confidence intervals will have the proper coverage probability. Our
results are applicable in a wide variety of MCMC estimation settings, such as
expectation, quantile, or simultaneous multivariate estimation. Finally, we
investigate the finite sample properties through a variety of examples and
provide some recommendations to practitioners.Comment: 24 page
Markov Chain Monte Carlo: Can We Trust the Third Significant Figure?
Current reporting of results based on Markov chain Monte Carlo computations
could be improved. In particular, a measure of the accuracy of the resulting
estimates is rarely reported. Thus we have little ability to objectively assess
the quality of the reported estimates. We address this issue in that we discuss
why Monte Carlo standard errors are important, how they can be easily
calculated in Markov chain Monte Carlo and how they can be used to decide when
to stop the simulation. We compare their use to a popular alternative in the
context of two examples.Comment: Published in at http://dx.doi.org/10.1214/08-STS257 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Fixed-width output analysis for Markov chain Monte Carlo
Markov chain Monte Carlo is a method of producing a correlated sample in
order to estimate features of a target distribution via ergodic averages. A
fundamental question is when should sampling stop? That is, when are the
ergodic averages good estimates of the desired quantities? We consider a method
that stops the simulation when the width of a confidence interval based on an
ergodic average is less than a user-specified value. Hence calculating a Monte
Carlo standard error is a critical step in assessing the simulation output. We
consider the regenerative simulation and batch means methods of estimating the
variance of the asymptotic normal distribution. We give sufficient conditions
for the strong consistency of both methods and investigate their finite sample
properties in a variety of examples
Batch means and spectral variance estimators in Markov chain Monte Carlo
Calculating a Monte Carlo standard error (MCSE) is an important step in the
statistical analysis of the simulation output obtained from a Markov chain
Monte Carlo experiment. An MCSE is usually based on an estimate of the variance
of the asymptotic normal distribution. We consider spectral and batch means
methods for estimating this variance. In particular, we establish conditions
which guarantee that these estimators are strongly consistent as the simulation
effort increases. In addition, for the batch means and overlapping batch means
methods we establish conditions ensuring consistency in the mean-square sense
which in turn allows us to calculate the optimal batch size up to a constant of
proportionality. Finally, we examine the empirical finite-sample properties of
spectral variance and batch means estimators and provide recommendations for
practitioners.Comment: Published in at http://dx.doi.org/10.1214/09-AOS735 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Hastings-Metropolis algorithm on Markov chains for small-probability estimation
Shielding studies in neutron transport, with Monte Carlo codes, yield
challenging problems of small-probability estimation. The particularity of
these studies is that the small probability to estimate is formulated in terms
of the distribution of a Markov chain, instead of that of a random vector in
more classical cases. Thus, it is not straightforward to adapt classical
statistical methods, for estimating small probabilities involving random
vectors, to these neutron-transport problems. A recent interacting-particle
method for small-probability estimation, relying on the Hastings-Metropolis
algorithm, is presented. It is shown how to adapt the Hastings-Metropolis
algorithm when dealing with Markov chains. A convergence result is also shown.
Then, the practical implementation of the resulting method for
small-probability estimation is treated in details, for a Monte Carlo shielding
study. Finally, it is shown, for this study, that the proposed
interacting-particle method considerably outperforms a simple-Monte Carlo
method, when the probability to estimate is small.Comment: 33 page
Regenerative Simulation for Queueing Networks with Exponential or Heavier Tail Arrival Distributions
Multiclass open queueing networks find wide applications in communication,
computer and fabrication networks. Often one is interested in steady-state
performance measures associated with these networks. Conceptually, under mild
conditions, a regenerative structure exists in multiclass networks, making them
amenable to regenerative simulation for estimating the steady-state performance
measures. However, typically, identification of a regenerative structure in
these networks is difficult. A well known exception is when all the
interarrival times are exponentially distributed, where the instants
corresponding to customer arrivals to an empty network constitute a
regenerative structure. In this paper, we consider networks where the
interarrival times are generally distributed but have exponential or heavier
tails. We show that these distributions can be decomposed into a mixture of
sums of independent random variables such that at least one of the components
is exponentially distributed. This allows an easily implementable embedded
regenerative structure in the Markov process. We show that under mild
conditions on the network primitives, the regenerative mean and standard
deviation estimators are consistent and satisfy a joint central limit theorem
useful for constructing asymptotically valid confidence intervals. We also show
that amongst all such interarrival time decompositions, the one with the
largest mean exponential component minimizes the asymptotic variance of the
standard deviation estimator.Comment: A preliminary version of this paper will appear in Proceedings of
Winter Simulation Conference, Washington, DC, 201
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