39 citations · 91 across the 12 of their papers we have counts for
12 papers
Projected Bayesian Spatial Factor Models
Lu Zhang
Factor models balance flexibility, identifiability, and computational efficiency, with Bayesian spatial factor models particularly prone to identifiability challenges and scaling l…
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner +3
The generality and robustness of inference algorithms is critical to the success of widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl. When de…
Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking
Soumyakanti Pan, Lu Zhang, Jonathan R. Bradley +1
Analysing non-Gaussian spatial-temporal data requires introducing spatial as well as temporal dependence in generalised linear models through the link function of an exponential fa…
Bayesian Modeling of Incompatible Spatial Data: A Case Study Involving Post-Adrian Storm Forest Damage Assessment
Lu Zhang, Andrew O. Finley, Arne Nothdurft +1
Modeling incompatible spatial data, i.e., data with different spatial resolutions, is a pervasive challenge in remote sensing data analysis. Typical approaches to addressing this c…
Applications of Conjugate Gradient in Bayesian computation
Lu Zhang
Conjugate gradient is an efficient algorithm for solving large sparse linear systems. It has been utilized to accelerate the computation in Bayesian analysis for many large-scale p…
Bayesian Geostatistics Using Predictive Stacking
Lu Zhang, Wenpin Tang, Sudipto Banerjee
We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct sp…