23 citations · 55 across the 10 of their papers we have counts for
11 papers
Generative Filtering for Recursive Bayesian Inference with Streaming Data
Ian Taylor, Andee Kaplan, Brenda Betancourt
In the streaming data setting, where data arrive continuously or in frequent batches and there is no pre-determined amount of total data, Bayesian models can employ recursive updat…
Fast Bayesian Record Linkage for Streaming Data Contexts
Ian Taylor, Andee Kaplan, Brenda Betancourt
Record linkage is the task of combining records from multiple files which refer to overlapping sets of entities when there is no unique identifying field. In streaming record linka…
Interactive Exploration of Large Dendrograms with Prototypes
Andee Kaplan, Jacob Bien
Hierarchical clustering is one of the standard methods taught for identifying and exploring the underlying structures that may be present within a data set. Students are shown exam…
Tracking the Transmission Dynamics of COVID-19 with a Time-Varying Coefficient State-Space Model
Joshua P. Keller, Tianjian Zhou, Andee Kaplan +2
The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-1…
d-blink: Distributed End-to-End Bayesian Entity Resolution
Neil G. Marchant, Andee Kaplan, Daniel N. Elazar +2
Entity resolution (ER; also known as record linkage or de-duplication) is the process of merging noisy databases, often in the absence of unique identifiers. A major advancement in…
A Practical Approach to Proper Inference with Linked Data
Andee Kaplan, Brenda Betancourt, Rebecca C. Steorts
Entity resolution (ER), comprising record linkage and de-duplication, is the process of merging noisy databases in the absence of unique identifiers to remove duplicate entities. O…
Simulating Markov random fields with a conclique-based Gibbs sampler
Andee Kaplan, Mark S. Kaiser, Soumendra N. Lahiri +1
For spatial and network data, we consider models formed from a Markov random field (MRF) structure and the specification of a conditional distribution for each observation. Fast si…
On the instability and degeneracy of deep learning models
Andee Kaplan, Daniel Nordman, Stephen Vardeman
A probability model exhibits instability if small changes in a data outcome result in large, and often unanticipated, changes in probability. This instability is a property of the…
Properties and Bayesian fitting of restricted Boltzmann machines
Andee Kaplan, Daniel Nordman, Stephen Vardeman
A restricted Boltzmann machine (RBM) is an undirected graphical model constructed for discrete or continuous random variables, with two layers, one hidden and one visible, and no c…
Putting Down Roots: A Graphical Exploration of Community Attachment
Andee Kaplan, Eric Hare
In this paper, we explore the relationships that individuals have with their communities. This work was prepared as part of the ASA Data Expo '13 sponsored by the Graphics Section…
Designing Modular Software: A Case Study in Introductory Statistics
Eric Hare, Andee Kaplan
Modular programming is a development paradigm that emphasizes self-contained, flexible, and independent pieces of functionality. This practice allows new features to be seamlessly…