Dynamical properties of strongly interacting Markov chains

Dynamical properties of strongly interacting Markov chains

Spatial interdependences of multiple stochastic units can be suitably quantified by the Kullback-Leibler divergence of the joint probability distribution from the corresponding factorized distribution. In the present paper, a generalized measure for stochastic interaction, which also captures temporal interdependences, is analysed within the setting of Markov chains. The dynamical properties of...

F Ur Mathematik in Den Naturwissenschaften Leipzig Dynamical Properties of Strongly Interacting Markov Chains Dynamical Properties of Strongly Interacting Markov Chains

Spatial interdependencies of stochastic units are usually quantiied by the Kullback-Leibler divergence of the joint probability distribution from the corresponding factorized distribution. In the present paper, a generalized measure for stochastic interaction, which also captures temporal interdependencies, is analysed within the setting of Markov chains. The dynamical properties of systems wit...

Properties of Markov Chains

Vulnerability of networks of interacting Markov chains.

The concept of vulnerability is introduced for a model of random, dynamical interactions on networks. In this model, known as the influence model, the nodes are arranged in an arbitrary network, while the evolution of the status at a node is according to an internal Markov chain, but with transition probabilities that depend not only on the current status of that node but also on the statuses o...

Learning Influence among Interacting Markov Chains

We present a model that learns the influence of interacting Markov chains within a team. The proposed model is a dynamic Bayesian network (DBN) with a two-level structure: individual-level and group-level. Individual level models actions of each player, and the group-level models actions of the team as a whole. Experiments on synthetic multi-player games and a multi-party meeting corpus show th...