An Importance Sampling Scheme on Dual Factor Graphs. I. Models in a Strong External Field

2014/01/20 by Mehdi Molkaraie, Molkaraie, Mehdi
Computer Science · Mathematics · Physics and Astronomy · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Physical sciences #Information Theory (cs.IT) #Markov Chains and Monte Carlo Methods #Random Matrices and Applications #Statistical Mechanics (cond-mat.stat-mech) #Stochastic processes and statistical mechanics #cond-mat.stat-mech #cs.IT #math.IT #stat.CO

paper · pdf · doi:10.48550/arxiv.1401.4912

9 pages

openalex publication_date 2014/01/20 · arxiv created 2015/02/05 · arxiv updated 2015/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose an importance sampling scheme to estimate the partition function of the two-dimensional ferromagnetic Ising model and the two-dimensional ferromagnetic q-state Potts model, both in the presence of an external magnetic field. The proposed scheme operates in the dual Forney factor graph and is capable of efficiently computing an estimate of the partition function under a wide range of model parameters. In particular, we consider models that are in a strong external magnetic field.

Citations

• Annealed importance sampling
• Crystal Statistics. I. A Two-Dimensional Model with an Order-Disorder Transition

Related

• An Importance Sampling Algorithm for the Ising Model with Strong Couplings
• Stratified Sampling for the Ising Model: A Graph-Theoretic Approach
• Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions
• Partition function and magnetization of two-dimensional Ising models in non-zero magnetic field: A semi-empirical approach
• Partition function for two-dimensional nearest neighbour Ising model in the presence of external magnetic field