We consider continuous space–time decay–surge population models, which are semi-stochastic processes for which deterministically declining populations, bound to fade away, are reinvigorated at random times by bursts or surges of random sizes. In a particular separable framework (in a sense made precise below) we provide explicit formulae for the scale (or harmonic) function and the speed measure of the process. The behavior of the scale function at infinity allows us to formulate conditions under which such processes either explode or are transient at infinity, or Harris recurrent. A description of the structures of both the discrete-time embedded chain and extreme record chain of such continuous-time processes is supplied.

A measure on a locally compact group is said to be spread out if one of its convolution powers is not singular with respect to Haar measure. Using Markov chain theory, we conduct a detailed analysis of random walks on homogeneous spaces with spread out increment distribution. For finite volume spaces, we arrive at a complete picture of the asymptotics of the n-step distributions: they equidistribute towards Haar measure, often exponentially fast and locally uniformly in the starting position. In addition, many classical limit theorems are shown to hold. In the infinite volume case, we prove recurrence and a ratio limit theorem for symmetric spread out random walks on homogeneous spaces of at most quadratic growth. This settles one direction in a long-standing conjecture.

Consider a strong Markov process in continuous time, taking values in some Polish statespace. Recently, Douc et al. [Stoc. Proc. Appl.119, (2009) 897–923] introduced verifiable conditions in terms ofa supermartingale property implying an explicit control of modulated moments of hittingtimes. We show how this control can be translated into a control of polynomial moments ofabstract regeneration times which are obtained by using the regeneration method ofNummelin, extended to the time-continuous context. As a consequence, if ap-th moment of the regeneration times exists, we obtain non asymptoticdeviation bounds of the form

\begin{equation*} P_{\nu} \left(\left|\frac1t\int_0^tf(X_s){\rm d}s-\mu(f)\right|\geq\ge\right)\leqK(p)\frac1{t^{p- 1}}\frac 1{\ge^{2(p-1)}}\|f\|_\infty^{2(p-1)} ,\quad p \geq 2.\end{equation*}${\mathit{P}}_{\mathit{\nu }}\left(\left|\frac{\mathrm{1}}{\mathit{t}}{\mathrm{\int }}_{\mathrm{0}}^{\mathit{t}}\mathit{f}\mathrm{\left(}{\mathit{X}}_{\mathit{s}}\mathrm{\right)}\mathrm{d}\mathit{s}\mathrm{-}\mathit{\mu }\mathrm{\left(}\mathit{f}\mathrm{\right)}\right|\mathrm{\ge }\mathit{\epsilon }\right)\mathrm{\le }\mathit{K}\mathrm{\left(}\mathit{p}\mathrm{\right)}\frac{\mathrm{1}}{{\mathit{t}}^{\mathit{p}\mathrm{-}\mathrm{1}}}\frac{\mathrm{1}}{{\mathit{\epsilon }}^{\mathrm{2}\mathrm{(}\mathit{p}\mathrm{-}\mathrm{1}\mathrm{)}}}\mathrm{\parallel }\mathit{f}{\mathrm{\parallel }}_{\mathrm{\infty }}^{\mathrm{2}\mathrm{(}\mathit{p}\mathrm{-}\mathrm{1}\mathrm{)}}\mathit{,}\mathit{p}\mathrm{\ge }\mathrm{2.}$

This paper establishes new Foster-type criteria for a Markov chain on a general state space to be Harris recurrent, positive Harris recurrent, or geometrically ergodic. The criteria are based on drift conditions involving stopping times rather than deterministic steps. Meyn and Tweedie (1994) developed similar criteria involving random-sized steps, independent of the Markov chain under study. They also posed an open problem of finding criteria involving stopping times. Our results essentially solve that problem. We also show that the assumption of ψ-irreducibility is not needed when stating our drift conditions for positive Harris recurrence or geometric ergodicity.

We define a class of tessellation models based on perturbing or deforming standard tessellations such as the Voronoi tessellation. We show how distributions over this class of ‘deformed’ tessellations can be used to define prior distributions for models based on tessellations, and how inference for such models can be carried out using Markov chain Monte Carlo methods; stability properties of the algorithms are investigated. Our approach applies not only to fixed dimension problems, but also to variable dimension problems, in which the number of cells in the tessellation is unknown. We illustrate our methods with two real examples. The first relates to reconstructing animal territories, represented by the individual cells of a tessellation, from observation of an inhomogeneous Poisson point process. The second example involves the analysis of an image of a cross-section through a sample of metal, with the tessellation modelling the micro-crystalline structure of the metal.

Let Φ = {Φ n} be an aperiodic, positive recurrent Markov chain on a general state space, π its invariant probability measure and f ≧ 1. We consider the rate of (uniform) convergence of Ex[g(Φ n)] to the stationary limit π (g) for |g| ≦ f: specifically, we find conditions under which

as n →∞, for suitable subgeometric rate functions r. We give sufficient conditions for this convergence to hold in terms of

(i) the existence of suitably regular sets, i.e. sets on which (f, r)-modulated hitting time moments are bounded, and

(ii) the existence of (f, r)-modulated drift conditions (Foster–Lyapunov conditions).

The results are illustrated for random walks and for more general state space models.

We consider GI/G/1 queues in an environment which is periodic in the sense that the service time of the nth customer and the next interarrival time depend on the phase θ n at the arrival instant. Assuming Harris ergodicity of {θ n} and a suitable condition on the traffic intensity, various Markov chains related to the queue are then again Harris ergodic and provide limit results for the standard customer- and time-dependent processes such as waiting times and queue lengths. As part of the analysis, a result of Nummelin (1979) concerning Lindley processes on a Markov chain is reconsidered.