3 papers
cs.SI2019
Latent Channel Networks
Clifford Anderson-Bergman, Phan Nguyen, Jose Cadena Pico
Latent Euclidean embedding models a given network by representing each node in a Euclidean space, where the probability of two nodes sharing an edge is a function of the distances…
stat.ML2018
XPCA: Extending PCA for a Combination of Discrete and Continuous Variables
Clifford Anderson-Bergman, Tamara G. Kolda, Kina Kincher-Winoto
Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. In this paper, we consider the question of how to handle heterogeneo…
stat.CO2015
Automated Parameter Blocking for Efficient Markov-Chain Monte Carlo Sampling
Daniel Turek, Perry de Valpine, Christopher J. Paciorek +1
Markov chain Monte Carlo (MCMC) sampling is an important and commonly used tool for the analysis of hierarchical models. Nevertheless, practitioners generally have two options for…