14 citations · 27 across the 6 of their papers we have counts for
6 papers
Parallel Markov Chain Monte Carlo for Bayesian Hierarchical Models with Big Data, in Two Stages
Zheng Wei, Erin M. Conlon
Due to the escalating growth of big data sets in recent years, new Bayesian Markov chain Monte Carlo (MCMC) parallel computing methods have been developed. These methods partition…
Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel computing methods
Konstandinos Kotsiopoulos, Alexey Miroshnikov, Erin Conlon
In this article we perform an asymptotic analysis of parallel Bayesian logspline density estimators. Such estimators are useful for the analysis of datasets that are partitioned in…
Parallel Computing for Copula Parameter Estimation with Big Data: A Simulation Study
Zheng Wei, Daeyoung Kim, Erin Marie Conlon
Copula-based modeling has seen rapid advances in recent years. However, in big data applications, the lengthy computation time for estimating copula parameters is a major difficult…
Parallel Markov Chain Monte Carlo for Non-Gaussian Posterior Distributions
Alexey Miroshnikov, Erin M. Conlon
Recent developments in big data and analytics research have produced an abundance of large data sets that are too big to be analyzed in their entirety, due to limits on computer me…
BayesSummaryStatLM: An R package for Bayesian Linear Models for Big Data and Data Science
Alexey Miroshnikov, Evgeny Savel'ev, Erin M. Conlon
Recent developments in data science and big data research have produced an abundance of large data sets that are too big to be analyzed in their entirety, due to limits on either c…
parallelMCMCcombine: An R Package for Bayesian Methods for Big Data and Analytics
Alexey Miroshnikov, Erin Conlon
Recent advances in big data and analytics research have provided a wealth of large data sets that are too big to be analyzed in their entirety, due to restrictions on computer memo…