317 citations · 721 across the 36 of their papers we have counts for
7 papers · 1 filter
Finite mixture models do not reliably learn the number of components
Diana Cai, Trevor Campbell, Tamara Broderick
Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. A common suggestion is to use a finite mixture model (…
Truncated Simulation and Inference in Edge-Exchangeable Networks
Xinglong Li, Trevor Campbell
Edge-exchangeable probabilistic network models generate edges as an i.i.d.~sequence from a discrete measure, providing a simple means for statistical inference of latent network pr…
Local Exchangeability
Trevor Campbell, Saifuddin Syed, Chiao-Yu Yang +2
Exchangeability -- in which the distribution of an infinite sequence is invariant to reorderings of its elements -- implies the existence of a simple conditional independence struc…
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak +1
Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is…
Exchangeable Trait Allocations
Trevor Campbell, Diana Cai, Tamara Broderick
Trait allocations are a class of combinatorial structures in which data may belong to multiple groups and may have different levels of belonging in each group. Often the data are a…
Edge-exchangeable graphs and sparsity
Tamara Broderick, Diana Cai
A known failing of many popular random graph models is that the Aldous-Hoover Theorem guarantees these graphs are dense with probability one; that is, the number of edges grows qua…
Truncated Random Measures
Trevor Campbell, Jonathan H. Huggins, Jonathan P. How +1
Completely random measures (CRMs) and their normalizations are a rich source of Bayesian nonparametric priors. Examples include the beta, gamma, and Dirichlet processes. In this pa…