Induced topological pressure for countable state Markov shifts

Mathematics – Dynamical Systems

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

28 pages

Scientific paper

We introduce the notion of induced topological pressure for countable state Markov shifts with respect to a non-negative scaling function and an arbitrary subset of finite words. Firstly, the scaling function allows a direct access to important thermodynamical quantities, which are usually given only implicitly by certain identities involving the classically defined pressure. In this context we generalise Savchenko's definition of entropy for special flows to a corresponding notion of topological pressure and show that this new notion coincides with the induced pressure for a large class of H\"older continuous height functions not necessarily bounded away from zero. Secondly, the dependence on the subset of words gives rise to interesting new results connecting the Gurevi{\vc} and the classical pressure with exhausting principles for a large class of Markov shifts. In this context we consider dynamical group extentions to demonstrate that our new approach provides a useful tool to characterise amenability of the underlying group structure.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Induced topological pressure for countable state Markov shifts does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Induced topological pressure for countable state Markov shifts, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Induced topological pressure for countable state Markov shifts will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-659057

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.