Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Gibbs sampling on factor graphs is a widely used inference technique, which often produces good empirical results. Theoretical guarantees for its performance are ak: even for tree structured graphs, the mixing time of Gibbs may be exponential in the number of variables. To help understand the behavior of Gibbs sampling,  introduce a new (hyper)graph property, called hierarchy width.  show that under suitable conditions on the ights, bounded hierarchy width ensures polynomial mixing time. Our study of hierarchy width is in part motivated by a class of factor graph templates, hierarchical templates, which have bounded hierarchy width—regardless of the data used to instantiate them.  demonstrate a rich application from natural language processing in which Gibbs sampling provably mixes rapidly and achieves accuracy that exceeds human volunteers.
['Christopher De Sa', 'Ce Zhang', 'Kunle Olukotun', 'Christopher Ré']
I am writing a research paper on a idea Rapidly mixing Gibbs sampling for a class of factor graphs using hierarchy width
[{"from": "human", "value": "I am writing a research paper on a idea Rapidly mixing Gibbs sampling for a class of factor graphs using hierarchy width"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Gibbs sampling on factor graphs is a widely used inference technique, which often produces good empirical results. Theoretical guarantees for its performance are ak: even for tree structured graphs, the mixing time of Gibbs may be exponential in the number of variables. To help understand the behavior of Gibbs sampling,  introduce a new (hyper)graph property, called hierarchy width.  show that under suitable conditions on the ights, bounded hierarchy width ensures polynomial mixing time. Our study of hierarchy width is in part motivated by a class of factor graph templates, hierarchical templates, which have bounded hierarchy width\u2014regardless of the data used to instantiate them.  demonstrate a rich application from natural language processing in which Gibbs sampling provably mixes rapidly and achieves accuracy that exceeds human volunteers."}]