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Max-and-Smooth: a two-step approach for approximate Bayesian inference
  in latent Gaussian models
v1v2 (latest)

Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models

27 July 2019
B. Hrafnkelsson
S. Siegert
Raphael Huser
H. Bakka
Árni V. Jóhannesson
ArXiv (abs)PDFHTML

Papers citing "Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models"

3 / 3 papers shown
Title
Regression modelling of spatiotemporal extreme U.S. wildfires via
  partially-interpretable neural networks
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
J. Richards
Raphael Huser
278
14
0
16 Aug 2022
Fast and Scalable Inference for Spatial Extreme Value Models
Fast and Scalable Inference for Spatial Extreme Value Models
Mei-Ching Chen
R. Ramezan
Martin Lysy
44
1
0
13 Oct 2021
Implementing Approximate Bayesian Inference using Adaptive Quadrature:
  the aghq Package
Implementing Approximate Bayesian Inference using Adaptive Quadrature: the aghq Package
Alex Stringer
TPM
35
6
0
12 Jan 2021
1