Papers
Topics
Authors
Recent
AI Research Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 83 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 25 tok/s Pro
GPT-5 High 30 tok/s Pro
GPT-4o 92 tok/s Pro
Kimi K2 174 tok/s Pro
GPT OSS 120B 462 tok/s Pro
Claude Sonnet 4 39 tok/s Pro
2000 character limit reached

Stochastic sequences with a regenerative structure that may depend both on the future and on the past (1212.1475v2)

Published 6 Dec 2012 in math.PR

Abstract: Many regenerative arguments in stochastic processes use random times which are akin to stopping times, but which are determined by the future as well as the past behaviour of the process of interest. Such arguments based on "conditioning on the future" are usually developed in an ad-hoc way in the context of the application under consideration, thereby obscuring underlying structure. In this paper we give a simple, unified and more general treatment of such conditioning theory. We further give a number of novel applications to various particle system models, in particular to various flavours of contact processes and to infinite-bin models. We give a number of new results for existing and new models. We further make connections with the theory of Harris ergodicity.

Summary

We haven't generated a summary for this paper yet.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.