English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Gibbs sampler is a particularly popular Markov chain used for learningand inference problems in Graphical Models (GMs). These tasks arecomputationally intractable in general, and the Gibbs sampler often suffersfrom slow mixing. In this paper, we study the Swendsen-Wang dynamics which is amore sophisticated Markov chain designed to overcome bottlenecks that impedethe Gibbs sampler. We prove O(\log n) mixing time for attractive binarypairwise GMs (i.e., ferromagnetic Ising models) on stochastic partitionedgraphs having n vertices, under some mild conditions, including low temperatureregions where the Gibbs sampler provably mixes exponentially slow. Ourexperiments also confirm that the Swendsen-Wang sampler significantlyoutperforms the Gibbs sampler when they are used for learning parameters ofattractive GMs.

Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models