
A Gibbs sampler for a class of random convex polytopes
We present a Gibbs sampler to implement the DempsterShafer (DS) theory ...
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Discussion on "Sparse graphs using exchangeable random measures" by Francois Caron and Emily B. Fox
This is a discussion on "Sparse graphs using exchangeable random measure...
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Comments on: A Gibbs sampler for a class of random convex polytopes
In this comment we discuss relative strengths and weaknesses of simplex ...
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Convergence rate of a collapsed Gibbs sampler for crossed random effects models
In this paper, we analyze the convergence rate of a collapsed Gibbs samp...
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Herded Gibbs Sampling
The Gibbs sampler is one of the most popular algorithms for inference in...
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Interdependent Gibbs Samplers
Gibbs sampling, as a model learning method, is known to produce the most...
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Approximating the Spectral Gap of the PólyaGamma Gibbs Sampler
The selfadjoint, positive Markov operator defined by the PólyaGamma Gi...
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Discussion of "A Gibbs sampler for a class of random convex polytopes"
An exciting new algorithmic breakthrough has been advanced for how to carry out inferences in a DempsterShafer (DS) formulation of a categorical data generating model. The developed sampling mechanism, which draws on theory for directed graphs, is a clever and remarkable achievement, as this has been an open problem for many decades. In this discussion, I comment on important contributions, central questions, and prevailing matters of the article.
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