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We introduce a natural conjugate prior for the transition matrix of a reversible Markov chain. This allows estimation and testing. The prior arises from random walk with reinforcement in the same way the Dirichlet prior arises from Pólya's urn. We give closed form normalizing constants, a simple method of simulation from the posterior and a characterization along the lines of W. E. Johnson's characterization of the Dirichlet prior.

We consider a linearly edge-reinforced random walk on a class of two-dimensional graphs with constant initial weights. The graphs are obtained from ℤ² by replacing every edge by a sufficiently large, but fixed number of edges in series. We prove that the linearly edge-reinforced random walk on these graphs is recurrent. Furthermore, we derive bounds for the probability that the edge-reinforced random walk hits the boundary of a large box before returning to its starting point.

In the present paper, linearly edge-reinforced random walk is studied on a large class of one-dimensional periodic graphs satisfying a certain reflection symmetry. It is shown that the edge-reinforced random walk is recurrent. Estimates for the position of the random walker are given. The edge-reinforced random walk has a unique representation as a random walk in a random environment, where the random environment is given by random weights on the edges. It is shown that these weights decay exponentially in space. The distribution of the random weights equals the distribution of the asymptotic proportion of time spent by the edge-reinforced random walker on the edges of the graph. The results generalize work of the authors in Merkl and Rolles (Ann Probab 33(6):2051–2093, 2005; 35(1):115–140, 2007) and Rolles (Probab Theory Related Fields 135(2):216–264, 2006) to a large class of graphs and to periodic initial weights with a reflection symmetry.

In this paper, we define an extension of the supersymmetric hyperbolic nonlinear sigma model introduced by Zirnbauer. We show that it arises as a weak joint limit of a time-changed version introduced by Sabot and Tarrès of the vertex-reinforced jump process. It describes the asymptotics of rescaled crossing numbers, rescaled fluctuations of local times, asymptotic local times on a logarithmic scale, endpoints of paths, and last exit trees.

We consider linearly edge-reinforced random walk on an arbitrary locally finite connected graph. It is shown that the process has the same distribution as a mixture of reversible Markov chains, determined by time-independent strictly positive weights on the edges. Furthermore, we prove bounds for the random weights, uniform, among others, in the size of the graph. [PUBLICATION ABSTRACT]

In the original publication, “Acknowledgements” section was incorrect. The correct “Acknowledgements” is given below.

We prove that the edge-reinforced random walk on the ladder Z× {1,2} with initial weights a > 3/4 is recurrent. The proof uses a known representation of the edge-reinforced random walk on a finite piece of the ladder as a random walk in a random environment. This environment is given by a marginal of a multicomponent Gibbsian process. A transfer operator technique and entropy estimates from statistical mechanics are used to analyze this Gibbsian process. Furthermore, we prove spatially exponentially fast decreasing bounds for normalized local times of the edge-reinforced random walk on a finite piece of the ladder, uniformly in the size of the finite piece.

Benjamini asked whether the scenery reconstruction methods of Matzinger (see e.g. [21], [22], [20]) can be done in polynomial time. In this article, we give the following answer for a 2-color scenery and simple random walk with holding: We prove that a piece of the scenery of length of the order 3 n around the origin can be reconstructed - up to a reflection and a small translation - with high probability from the first 2 . 310 ?n observations with a constant ? > 0 independent of n. Thus, the number of observations needed is polynomial in the length of the piece of scenery which we reconstruct. The probability that the reconstruction fails tends to 0 as n??. In contrast to [21], [22], and [20], the proofs in this article are all constructive. Our reconstruction algorithm is an algorithm in the sense of computer science. This is the first article which shows that the scenery reconstruction is also possible in the 2-color case with holding. The case with holding is much more difficult than [22] and requires completely different methods. [PUBLICATION ABSTRACT]

In this article, we study linearly edge-reinforced random walk on general multi-level ladders for large initial edge weights. For infinite ladders, we show that the process can be represented as a random walk in a random environment, given by random weights on the edges. The edge weights decay exponentially in space. The process converges to a stationary process. We provide asymptotic bounds for the range of the random walker up to a given time, showing that it localizes much more than an ordinary random walker. The random environment is described in terms of an infinite-volume Gibbs measure.

We give a characterization of a modified edge-reinforced random walk in terms of certain partially exchangeable sequences. In particular, we obtain a characterization of an edge-reinforced random walk (introduced by Coppersmith and Diaconis) on a 2-edge-connected graph. Modifying the notion of partial exchangeability introduced by Diaconis and Freedman in [3], we characterize unique mixtures of reversible Markov chains under a recurrence assumption.