10 citations · 14 across the 3 of their papers we have counts for
5 papers
Learning Loosely Connected Markov Random Fields
Rui Wu, R. Srikant, Jian Ni
We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is…
Fast Mixing of Parallel Glauber Dynamics and Low-Delay CSMA Scheduling
Libin Jiang, Mathieu Leconte, Jian Ni +2
Glauber dynamics is a powerful tool to generate randomized, approximate solutions to combinatorially difficult problems. It has been used to analyze and design distributed CSMA (Ca…
Mixing Time of Glauber Dynamics With Parallel Updates and Heterogeneous Fugacities
Mathieu Leconte, Jian Ni, R. Srikant
Glauber dynamics is a powerful tool to generate randomized, approximate solutions to combinatorially difficult problems. Applications include Markov Chain Monte Carlo (MCMC) simula…
Q-CSMA: Queue-Length Based CSMA/CA Algorithms for Achieving Maximum Throughput and Low Delay in Wireless Networks
Jian Ni, Bo Tan, R. Srikant
Recently, it has been shown that CSMA-type random access algorithms can achieve the maximum possible throughput in ad hoc wireless networks. However, these algorithms assume an ide…
Network Tomography Based on Additive Metrics
Jian Ni, Sekhar Tatikonda
Inference of the network structure (e.g., routing topology) and dynamics (e.g., link performance) is an essential component in many network design and management tasks. In this pap…