## Batch mean variance estimators for MCMC

**G**alin Jones and James Flegal posted on arXiv a new version of their batch mean convergence assessment paper. As posted earlier, I think this is a very interesting approach to MCMC convergence assessment and stopping rule, and I have included their method in the Convergence Assessment chapter of ** EnteR Monte Carlo Statistical Methods.** The stopping rule studied in the paper is of the form

where is an appropriate Student’s t quantile and is a positive function of order . The estimation of the asymptotic variance by is a delicate issue for MCMC output, which can be addressed by regenerative sampling when this is feasible, or by batch means, which is a general solution. The batch mean estimate of is given by

where . The overlapping batch mean estimator allows for the partial averages to use common simulations. Those estimators are consistent, but the overlapping batch mean estimator slightly decreases the variance. The paper also covers the case of the spectral approaches to estimators . As mentioned earlier, this is an interesting paper (at least) in that it relates theory (which was unknown to me) with practice, in the construction of proper stopping rules that apply in a general setting (modulo mathematical assumptions that may be hard to verify in practice but cannot be weakened).

September 22, 2011 at 12:11 am

[...] Jones. (As stressed in Introducing Monte Carlo Methods with R, I particularly like the idea of Flegal and Jones to validate a bootstrap approach to confidence evaluation!) The two next chapters are covering [...]

May 1, 2009 at 4:58 pm

The equations are difficult to see … maybe my eyes need testing. In the meantime … can a different colour be used?

Signed: Demanding blog reader!

May 1, 2009 at 5:58 pm

Uh?! Can’t you press the Ctrl-+ or something like that…?! (I just tested and indeed the equations come in one size only…) I can obviously rewrite equations in bright yellow but checking on the original paper would be easier… Thanks!