David Bolin @jdavidbolin
Assoc. professor of statistics at @cemseKAUST stochproc.kaust.edu.sa Joined December 2014-
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Interesting workshop which also will have a tutorial on the MetricGraph package.
Great work about finding hotspots for traffic accidents using the MetricGraph and excursions packages.
Great work about finding hotspots for traffic accidents using the MetricGraph and excursions packages.
Check out our latest paper on model checking for latent Gaussian models using R-INLA and STAN by @Rafael_M_Cabral , @jdavidbolin , and @HavardRue1 : arxiv.org/abs/2307.12365 We propose a workflow to check model assumptions and the robustness of the statistical answers. #RINLA
The paper on the approach that facilitates using SPDE models with arbitrary smoothness in @bayescomp_inla and @inlabru is now out in JCGS. The method recently won one of the 2023 KAUST competitions on Spatial Stats for Large Data, so it works really well. tandfonline.com/doi/full/10.10…
Huge congratulations 👏👏 to @Rafael_M_Cabral for successfully passing his PhD defense. He was advised by @jdavidbolin and @HavardRue1 and has made great contributions to latent non gaussian models.
After about one year of development, the MetricGraph #RStats package is now public! It facilitates working with data and random fields on linear networks and metric graphs, and has interfaces to @inlabru and @bayescomp_inla. For detailed tutorials, see davidbolin.github.io/MetricGraph/
Last year, @Jonas_Wallin, @AlexandreBSimas and I wrote a paper on Gaussian fields on metric graphs. It was a bit dense, so it is now split it in three parts: Regularity: arxiv.org/abs/2205.06163 Markov properties: arxiv.org/abs/2304.03190 Inference: arxiv.org/abs/2304.10372
📝Read our new post by R. Cabral, D. Bolin & H. Rue (@KAUST_News) on fitting robust non-Gaussian models in Stan and R-INLA. youngstats.github.io/post/2023/01/1… #statistics #blogpost @StatFen @BernoulliSoc @InstMathStat
Great work by Rafael Cabral (student of Håvard Rue and me at @cemseKAUST) on fitting latent non-Gaussian models.
Great work by Rafael Cabral (student of Håvard Rue and me at @cemseKAUST) on fitting latent non-Gaussian models.
Want to learn about time series + spatial applications in Stan? Come to our upcoming (virtual) 1-day "StanConnect: Stan Through Space + Time". Details below 👇🏽It's free! Talks + abstracts + registration here: eventbrite.com/e/stanconnect-…
Want to learn about time series + spatial applications in Stan? Come to our upcoming (virtual) 1-day "StanConnect: Stan Through Space + Time". Details below 👇🏽It's free! Talks + abstracts + registration here: eventbrite.com/e/stanconnect-…
Version 1.2.0 of rSPDE is now on CRAN. One new feature is the support for using fractional SPDE models in @inlabru. See davidbolin.github.io/rSPDE/articles… for details. #RINLA #rstats #rspatial
In December last year, @AlexandreBSimas and I updated the rSPDE package davidbolin.github.io/rSPDE/ with functions that facilitate using fractional SPDE models in R-INLA. The paper that describes the approach that was used to do this is now finally out: arxiv.org/abs/2209.04670
We're not slowing down with Expressing & Exploiting Structure in Modeling, Theory, & Computation w/ #Gaussian Processes, hearing talks by @jdavidbolin, @Princeton's Ruiyi Yang, @NorthwesternU's Matt Plumlee, @HoumanOwhadi, & @SandiaLabs's Mamikon Gulian imsi.institute/activities/gau…
Looking forward to attending NORDSTAT again next year, where I will be organising a session about Gaussian processes on networks and graphs.
Looking forward to attending NORDSTAT again next year, where I will be organising a session about Gaussian processes on networks and graphs.
Our paper on an ICA model for fMRI using surface-based spatial priors & empirical population priors was accepted at JCGS! Using spatial priors dramatically improves power to identify areas of the brain engaged in different functional networks. @jdavidbolin arxiv.org/abs/2005.13388
Thanks, it was a nice surprise!
New paper about Gaussian processes on metric graphs, such as street networks. With @Jonas_Wallin and Alexandre Simas. This took a long time to develop, but I am really happy about the outcome. R implementations for INLA etc are coming soon. arxiv.org/abs/2205.06163
Looking forward to giving a talk at #SIAMUQ22 today about our recent AOS paper on kriging and Gaussian processes.
My work with @mandyfmejia @jdavidbolin Yu (Ryan) Yue, and Sarah Ryan on the validation of the surface-based spatial Bayesian general linear model has just been accepted by Neuroimage! Software and the code for the analysis is on GitHub! sciencedirect.com/science/articl…

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