Characterizing optimal sampling of binary contingency tables via the configuration model
From MaRDI portal
Publication:4909199
DOI10.1002/rsa.20403zbMath1257.62065arXiv1007.1214OpenAlexW1546163884MaRDI QIDQ4909199
Alexandre Stauffer, Jose H. Blanchet
Publication date: 12 March 2013
Published in: Random Structures & Algorithms (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1007.1214
Related Items
On the mixing time of the Diaconis-Gangolli random walk on contingency tables over \(\mathbb{Z}/q\mathbb{Z} \) ⋮ The giant component of the directed configuration model revisited ⋮ The diameter of the directed configuration model ⋮ Rankings in directed configuration models with heavy tailed in-degrees ⋮ The probability that a random multigraph is simple. II ⋮ Limit laws for self-loops and multiple edges in the configuration model
Cites Work
- Unnamed Item
- The complexity of computing the permanent
- Asymptotic enumeration of sparse 0--1 matrices with irregular row and column sums
- Expansion properties of a random regular graph after random vertex deletions
- On the number of matrices and a random matrix with prescribed row and column sums and 0-1 entries
- Approximate counting, uniform generation and rapidly mixing Markov chains
- A probabilistic proof of an asymptotic formula for the number of labelled regular graphs
- Efficient importance sampling for binary contingency tables
- Asymptotic enumeration of dense 0-1 matrices with specified line sums
- A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries
- Accelerating Simulated Annealing for the Permanent and Combinatorial Counting Problems
- The Probability That a Random Multigraph is Simple
- A Sequential Algorithm for Generating Random Graphs
- Sampling contingency tables
- Generalized Monte Carlo significance tests
- Probability and Computing
- Asymptotics and random matrices with row-sum and column sum-restrictions
- Sequential Monte Carlo Methods for Statistical Analysis of Tables
- Sampling binary contingency tables with a greedy start
This page was built for publication: Characterizing optimal sampling of binary contingency tables via the configuration model
