1 code implementation • 16 Jul 2020 • Sirio Legramanti, Tommaso Rigon, Daniele Durante, David B. Dunson
The coexistence of these noisy block patterns limits the reliability of routinely-used community detection algorithms, and requires extensions of model-based solutions to realistically characterize the node partition process, incorporate information from node attributes, and provide improved strategies for estimation and uncertainty quantification.
no code implementations • 9 Jun 2020 • Tommaso Rigon, Amy H. Herring, David B. Dunson
Several existing clustering algorithms, including k-means, can be interpreted as generalized Bayes estimators under our framework, and hence we provide a method of uncertainty quantification for these approaches.
1 code implementation • 10 May 2017 • Daniele Durante, Antonio Canale, Tommaso Rigon
We propose a novel nested expectation-maximization algorithm for latent class models with covariates which allows maximization of the full-model log-likelihood and, differently from current methods, is characterized by a monotone log-likelihood sequence.
Computation Methodology
1 code implementation • 11 Jan 2017 • Tommaso Rigon, Daniele Durante
There is an increasing interest in learning how the distribution of a response variable changes with a set of predictors.
Computation