Many existing statistical models for networks overlook the fact that most real-world networks
are formed through a growth process. To address this, we introduce the PAPER (Preferential …

Traditional Bayesian random partition models assume that the size of each cluster grows
linearly with the number of data points. While this is appealing for some applications, this …

We introduce a class of generative network models that insert edges by connecting the
starting and terminal vertices of a random walk on the network graph. Within the taxonomy of …

Network growth processes can be understood as generative models of the structure and
history of complex networks. This point of view naturally leads to the problem of network …

In database management, record linkage aims to identify multiple records that correspond to
the same individual. Record linkage can be treated as a clustering problem in which one or …

Recent advances in Bayesian models for random partitions have led to the formulation and
exploration of Exchangeable Sequences of Clusters (ESC) models. Under ESC models, it is …

Many statistical models for networks overlook the fact that most real-world networks are
formed through a growth process. To address this, we introduce the Preferential Attachment …

Analytical explorations on complex networks and cubes (ie, multi-dimensional datasets) are
currently two separate research fields with different strategies. To gain more insights into …

STAT 547C Final Project Page 1 STAT 547C Final Project Benjamin Bloem-Reddy October
18, 2022 Logistics The final project of a project outline (due November 4 at 11:59 pm) and a …

Deep generative models have de facto emerged as state of the art when it comes to density
estimation and sampling high-dimensional and multi-modal data. They combine the abstract …