How does the Gibbs model work?
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The Gibbs model is a generative model used for clustering ensemble problems. It aims to output a clustering that minimizes the average distance to the input clusterings. The model is parameterized by a center clustering and a scale parameter. The probability of a particular clustering is determined by its scaled Rand distance to the center clustering, with a decay that follows an exponential function. The model has several interesting properties and can be used for sampling and reconstruction. The combinatorial structure of the Gibbs model for clusterings is more intricate and challenging than the corresponding model for permutations.
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The Gibbs-plaid model assumes a prior dependency between genes and conditions through a stochastic relational graph and estimates the posterior distribution of bicluster membership labels using a stochastic algorithm. | |
The Gibbs model for clustering ensembles is parameterized by a center clustering and a scale parameter, and the probability of a clustering decays exponentially with its scaled Rand distance to the center clustering. | |
The Gibbs-plaid model assumes a prior dependency between genes/conditions through a stochastic relational graph and estimates the posterior distribution of bicluster membership labels using a stochastic algorithm. | |
13 Nov 2018 1 Citations | The Gibbs model uses a statistical approach to determine the probability distribution of particle locations in equipment for mineral resource enrichment and separation. |
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