Bounding Stationary Expectations of Markov Processes

By Peter W. Glynn, Assaf J. Zeevi. 2008

80 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a2497291ca3d987cf06e2ab1bb1ff6e4002d0e7e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Ofir Nachum
2. Edgar A. Duéñez-Guzmán
3. Jim G. Dai
4. Mohammad Ghavamzadeh
5. Yinlam Chow
6. Anton Braverman

In Management Science and Operations Research

1. Ofir Nachum
2. Yinlam Chow
3. Jiekun Feng
4. Edgar A. Duéñez-Guzmán
5. Jim G. Dai
6. Anton Braverman
7. Mohammad Ghavamzadeh
8. Sergey Levine

In Statistics and Probability

1. Jiekun Feng
2. Jim G. Dai
3. Anton Braverman
4. Mohammad Ghavamzadeh
5. Yinlam Chow
6. Ofir Nachum
7. Lihu Xu

Citations in the corpus

• Stein's method for steady-state diffusion approximations: an introduction through the Erlang-A and Erlang-C models (2015, background): “1, and relies on Proposition 3 of [26].”
• Diffusion models and steady-state approximations for exponentially ergodic Markovian queues. (2014, unknown): “lim sup n→∞ π (|f |)≤ b δ (20) for all functions f with |f | ≤ V ; see also [14], Corollary 2.”
• A Lyapunov-based Approach to Safe Reinforcement Learning (2018, background): “Besides modeling physical energy, Lyapunov functions can also represent abstract quantities, such as the steady-state performance of a Markov process [15].”