This paper gathers together different conditions which are all equivalent to geometric ergodicity of time-homogeneous Markov chains on general state spaces. A total of 34 different conditions are presented (27 for general chains plus 7 for reversible chains), some old and some new, in terms of such notions as convergence bounds, drift conditions, spectral properties, etc., with different assumptions about the distance metric used, finiteness of function moments, initial distribution, uniformity of bounds, and more. Proofs of the connections between the different conditions are provided, somewhat self-contained but using some results from the literature where appropriate.

This paper gathers together different conditions which are all equivalent to geometric ergodicity of time-homogeneous Markov chains on general state spaces. A total of 34 different conditions are presented (27 for general chains plus 7 for reversible chains), some old and some new, in terms of such notions as convergence bounds, drift conditions, spectral properties, etc., with different assumptions about the distance metric used, finiteness of function moments, initial distribution, uniformity of bounds, and more. Proofs of the connections between the different conditions are provided, somewhat self-contained but using some results from the literature where appropriate.

In this work we consider a multivariate non-homogeneous Markov chain of order $$K \ge 0$$ K ≥ 0 to study the occurrences of exceedances of environmental thresholds. In the model, $$d \ge 1$$ d ≥ 1 pollutants may be observed and, according to their respective environmental thresholds, a pollutant’s concentration measurement may be considered an exceedance or not. The parameters of the model are the order of the chain, and its initial and transition distributions. These parameters are estimated under the Bayesian point of view with the maximum a posteriori and leave-one-out cross validation methods used to estimate the order. In the case of the initial and transition probabilities, the estimation is made through samples generated using their respective posterior distributions. Once these parameters are obtained, we may estimate the probability of having no, one or more pollutants exceeding the associated environmental thresholds. This is made using the Markov property as well as a recurrence formula. Results are applied to the case where $$d = 2$$ d = 2 which will correspond to ozone and particulate matter with diameter smaller than 10 microns (PM$$_{10}$$ 10 ) measurements obtained from the Mexico City monitoring network.