B1306
Title: Fast computing for latent Gaussian random field models
Authors: Murali Haran - The Pennsylvania State University (United States) [presenting]
Abstract: Latent Gaussian random field models are extremely popular in a wide variety of areas, including environmental science and infectious disease modeling. We will describe some computational efficient strategies for fitting such models within a Bayesian paradigm. Our approach applies to a wide array of latent Gaussian random field models, and permits the analysis of large data sets.
