We calculate the average size (i.e. number of arcs) of Markovgraphs for several classes of vertex maps on finite trees. These are include arbitrary maps, permutations, cyclic permutations and the so-called neighbourhood maps. In the latter case we obtain an explicit formula for the size of corresponding Markov graphs and then provide sharp bounds for it in terms of the underlying trees
AbstractThe Markov width of a graph is a graph invariant defined as the maximum degree of a Markov b...
We investigate scaling limits of several types of random trees. The study of scaling limits of rand...
We give an asymptotic expression for the expected number of spanning trees in a random graph with a...
© 2018 Elsevier B.V. DAG models are statistical models satisfying a collection of conditional indepe...
When learning a directed acyclic graph (DAG) model via observational data, one generally cannot iden...
When learning a directed acyclic graph (DAG) model via observational data, one generally cannot iden...
summary:The main focus of combinatorial dynamics is put on the structure of periodic points (and the...
A Markov chain approach to the study of randomly grown graphs is proposed and applied to some popula...
With every self-map on the vertex set of a finite tree one can associate the directed graph of a spe...
Abstract. Markov chains are a convenient means of generating real-izations of networks, since they r...
AbstractBayesian networks, equivalently graphical Markov models determined by acyclic digraphs or AD...
Since Euler began studying paths in graphs, graph theory has become an important branch of mathemati...
Abstract. We apply MCMC sampling to approximately calculate some quantities, and discuss their impli...
We show that the stationary distribution of a finite Markov chain can be expressed as the sum of cer...
Starting from any graph on {1,. .. , n}, consider the Markov chain where at each time-step a uniform...
AbstractThe Markov width of a graph is a graph invariant defined as the maximum degree of a Markov b...
We investigate scaling limits of several types of random trees. The study of scaling limits of rand...
We give an asymptotic expression for the expected number of spanning trees in a random graph with a...
© 2018 Elsevier B.V. DAG models are statistical models satisfying a collection of conditional indepe...
When learning a directed acyclic graph (DAG) model via observational data, one generally cannot iden...
When learning a directed acyclic graph (DAG) model via observational data, one generally cannot iden...
summary:The main focus of combinatorial dynamics is put on the structure of periodic points (and the...
A Markov chain approach to the study of randomly grown graphs is proposed and applied to some popula...
With every self-map on the vertex set of a finite tree one can associate the directed graph of a spe...
Abstract. Markov chains are a convenient means of generating real-izations of networks, since they r...
AbstractBayesian networks, equivalently graphical Markov models determined by acyclic digraphs or AD...
Since Euler began studying paths in graphs, graph theory has become an important branch of mathemati...
Abstract. We apply MCMC sampling to approximately calculate some quantities, and discuss their impli...
We show that the stationary distribution of a finite Markov chain can be expressed as the sum of cer...
Starting from any graph on {1,. .. , n}, consider the Markov chain where at each time-step a uniform...
AbstractThe Markov width of a graph is a graph invariant defined as the maximum degree of a Markov b...
We investigate scaling limits of several types of random trees. The study of scaling limits of rand...
We give an asymptotic expression for the expected number of spanning trees in a random graph with a...