A Bayesian latent space model that generates node embeddings via branching Brownian motion on a tree to infer hierarchical node structures in networks.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ME 2years
2025 2representative citing papers
A unified least squares framework for identifying and estimating causal effects in crossover designs that remains valid under misspecified working models.
citing papers explorer
-
Phylogenetic latent space models for network data
A Bayesian latent space model that generates node embeddings via branching Brownian motion on a tree to infer hierarchical node structures in networks.
-
Principled analysis of crossover designs: causal effects, efficient estimation, and robust inference
A unified least squares framework for identifying and estimating causal effects in crossover designs that remains valid under misspecified working models.