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Non-asymptotic confidence intervals for MCMC in practice

Using concentration inequalities, we give non-asymptotic confidence intervals for estimates obtained by Markov chain Monte Carlo (MCMC) simulations, when using the approximation $\mathbb{E}_π f\approx (1/(N-t_0))\cdot \sum_{i=t_0+1}^N f(X_i)$. To allow the application of non-asymptotic error bounds in practice, here we state bounds formulated in terms of the spectral properties of the chain and the properties of $f$ and propose estimators of the parameters appearing in the bounds, including the spectral gap, mixing time, and asymptotic variance. We introduce a method for setting the burn-in time and the initial distribution that is theoretically well-founded and yet is relatively simple to apply. We also investigate the estimation of $\mathbb{E}_πf$ via subsampling and by using parallel runs instead of a single run. Our results are applicable to both reversible and non-reversible Markov chains on discrete as well as general state spaces. We illustrate our methods by simulations for three examples of Bayesian inference in the context of risk models and clinical trials.