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UID:pretalx-juliacon-2026-3KTAGM@pretalx.com
DTSTART;TZID=CET:20260814T164500
DTEND;TZID=CET:20260814T171500
DESCRIPTION:Bayesian spatial modeling is critical across science\, yet most
  practitioners are locked into R due to R-INLA.\nWe present a Julia ecosys
 tem to change this: **GaussianMarkovRandomFields.jl** provides fast sparse
  precision-based inference via SPDE discretizations & more\, while **Integ
 ratedNestedLaplace.jl** brings the full INLA methodology to Julia with a f
 amiliar formula interface.\nWe demonstrate the ecosystem on spatial diseas
 e mapping\, showing competitive results with R-INLA and native Julia advan
 tages.
DTSTAMP:20260502T102650Z
LOCATION:Room 5
SUMMARY:Scalable Bayesian Spatial Modeling in Julia with GMRFs and INLA - T
 im Weiland
URL:https://pretalx.com/juliacon-2026/talk/3KTAGM/
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