Markov chain central limit theorem

In the mathematical theory of random processes, the Markov chain central limit theorem has a conclusion somewhat similar in form to that of the classic central limit theorem (CLT) of probability theory, but the quantity in the role taken by the variance in the classic CLT has a more complicated definition.

Statement

Suppose that:

• the sequence ${\textstyle X_{1},X_{2},X_{3},\ldots }$ of random elements of some set is a Markov chain that has a stationary probability distribution; and
• the initial distribution of the process, i.e. the distribution of ${\textstyle X_{1}}$, is the stationary distribution, so that ${\textstyle X_{1},X_{2},X_{3},\ldots }$ are identically distributed. In the classic central limit theorem these random variables would be assumed to be independent, but here we have only the weaker assumption that the process has the Markov property; and
• ${\textstyle g}$ is some (measurable) real-valued function for which ${\textstyle \operatorname {var} (g(X_{1}))<+\infty .}$

Now let

${\begin{aligned}\mu &=\operatorname {E} (g(X_{1})),\\\sigma ^{2}&=\operatorname {var} (g(X_{1}))+2\sum _{k=1}^{\infty }\operatorname {cov} (g(X_{1}),g(X_{1+k})),\\{\widehat {\mu }}_{n}&={\frac {1}{n}}\sum _{k=1}^{n}g(X_{k}).\end{aligned}}$

Then as ${\textstyle n\to \infty ,}$ we have

${\hat {\mu }}_{n}\approx \operatorname {Normal} \left(\mu ,{\frac {\sigma ^{2}}{n}}\right),$

or more precisely,

${\sqrt {n}}({\hat {\mu }}_{n}-\mu )\ {\xrightarrow {\mathcal {D}}}\ {\text{Normal}}(0,\sigma ^{2}),$

where the decorated arrow indicates convergence in distribution.

Use

The Markov chain central limit theorem can be used to justify estimation of ${\textstyle \mu =\operatorname {E} (g(X_{1}))}$ by Markov chain Monte Carlo MCMC methods, and provides bounds on the probable error of estimation.

References

• Gordin, M. I. and Lifšic, B. A. (1978). "Central limit theorem for stationary Markov processes." Soviet Mathematics, Doklady, 19, 392–394. (English translation of Russian original).
• Geyer, Charles J. (2011). "Introduction to MCMC." In Handbook of Markov Chain Monte Carlo, edited by S. P. Brooks, A. E. Gelman, G. L. Jones, and X. L. Meng. Chapman & Hall/CRC, Boca Raton, pp. 3–48.