20 citations · 27 across the 3 of their papers we have counts for
5 papers
Efficient model-based clustering with coalescents: Application to multiple outcomes using medical records data
Ricardo Henao, Joseph E. Lucas
We present a sequential Monte Carlo sampler for coalescent based Bayesian hierarchical clustering. The model is appropriate for multivariate non-\iid data and our approach offers a…
Sparse latent factor models with interactions: Analysis of gene expression data
Vinicius Diniz Mayrink, Joseph Edward Lucas
Sparse latent multi-factor models have been used in many exploratory and predictive problems with high-dimensional multivariate observations. Because of concerns with identifiabili…
Efficient hierarchical clustering for continuous data
Ricardo Henao, Joseph E. Lucas
We present an new sequential Monte Carlo sampler for coalescent based Bayesian hierarchical clustering. Our model is appropriate for modeling non-i.i.d. data and offers a substanti…
Bayesian Gaussian Copula Factor Models for Mixed Data
Jared S. Murray, David B. Dunson, Lawrence Carin +1
Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categoric…
Latent protein trees
Ricardo Henao, J. Will Thompson, M. Arthur Moseley +3
Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocess…