Tuning-Free Heterogeneous Inference in Massive Networks
Sign Up to like & getrecommendations! Published in 2019 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2018.1537920
Abstract: Abstract Heterogeneity is often natural in many contemporary applications involving massive data. While posing new challenges to effective learning, it can play a crucial role in powering meaningful scientific discoveries through the integration of information… read more here.
Keywords: heterogeneous inference; free heterogeneous; massive networks; tuning free ... See more keywords
Distributed Bayesian Inference in Massive Spatial Data
Sign Up to like & getrecommendations! Published in 2023 at "Statistical Science"
DOI: 10.1214/22-sts868
Abstract: Gaussian process (GP) regression is computationally expensive in spatial applications involving massive data. Various methods address this limitation, including a small number of Bayesian methods based on distributed computations (or the divide-and-conquer strategy). Focusing on… read more here.
Keywords: distributed bayesian; inference massive; posterior distribution; massive spatial ... See more keywords