# Statistics Colloquium, Dr. Abhirup Datta

## Department of Biostatistics, Johns Hopkins Bloomberg School

11:00 AM - 12:00 PM

**Title: **Large
scale spatial analysis using sparse Cholesky factors

**Abstract: **Gaussian process (GP) models are widely used for
analyzing space and space-time indexed data from forestry, environmental
health, disease epidemiology etc. However, traditional GP models entail
computations that become prohibitive for datasets with large number of spatial
or temporal locations. In this talk, I will present highly scalable alternative
models based on sparse Cholesky factors for analyzing massive spatial,
spatio-temporal and areal datasets. For spatial and spatio-temporal datasets, I
will introduce our proposed Nearest Neighbor Gaussian Process (NNGP) models
that offer a scalable fully model based approach which effectively reproduces
the corresponding inference from traditional (but highly expensive) GP models.
For areal datasets, I will discuss ongoing work on Directed Acyclic Graph
Autoregressive Model that provides an alternative to the widely used
Conditional Autoregressive Model. I will describe Matrix-free Markov chain
Monte Carlo (MCMC) algorithms for massive scalability. I will also discuss
applications large scale prediction of forest biomass and analysis of air
pollution data.