Ising modelMarkov Chain Monte CarloThe Ising model is one of the simplest and most famous models of interacting systems. It was originally proposed to model ferromagnetic interactions in statistical physics and is now widely used to model spatial processes in many areas such as ecology, sociology,...
Markov chain Monte Carlo is used essentially to estimate integrals in high dimensions. This article addresses the accuracy of such estimation. Through computer experiments performed on the two-dimensional Ising model, we compare the most common method for error estimates in statistical mechanics. It ...
This work presents an adaptive multi-GPU Exchange Monte Carlo approach for the simulation of the 3D Random Field Ising Model (RFIM). The design is based on a two-level parallelization. The first level, spin-level parallelism, maps the parallel computation as optimal 3D thread-blocks that simula...
They formulated a variational inference (VI) model and compared that to a Markov chain monte carlo (MCMC) method. MCMC proved to be slightly more accurate in several metrics but VI was very close. On the other hand, the variational method was 1000 times faster. It is also interesting to n...
It’s no wonder that sometimes I feel cognitively closer to my salad than my coworkers when the lunch conversation turns to Bayesian models, Markov chain analysis and Monte-Carlo simulations. Thankfully the lunch breaks I’m told have gotten shorter too. ...
Although the stratigraphic and phylogenetic rates here are assumed to be fully independent parameters (as in the incompatible rates model), we used their joint posterior distributions sampled using Markov Chain Monte Carlo (MCMC) to assess the support for each model. We ran 20 million MCMC ...
The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statistical modeling of biological data. In the standard setting, the parameters of an Ising model (couplings and fields) are infer...
Based on Graham’s (2012) recommendation, the Markov-Chain Monte Carlo (MCMC) method in SPSS 22 was used to generate 40 imputed data sets, with 50 iterations of MCMC between each imputation. Methodologists regard multiple imputation as a “state of the art” missing data technique because ...
The analysis was run twice over 10 million generations for the Markov Chain Monte Carlo (MCMC) algorithm, with a subsampling frequency of 200 generations and a ‘burn-in’ of 25%. Pairwise distances were generated by Mega-X 10.2.4 with a Kimura two-parameter substitution model with ...
#SSMC SSMC (Spin System Monte Carlo) is a free Monte Carlo simulation code for classical spin systems like the Ising model. Here are some of its features: 0. Models: nearest-neighbor Ising model in one and two dimensions, dipolar Ising model on two-dimensional square and honeycomb lattices,...