ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1202.1738
  4. Cited By
INLA or MCMC? A Tutorial and Comparative Evaluation for Spatial
  Prediction in log-Gaussian Cox Processes
v1v2 (latest)

INLA or MCMC? A Tutorial and Comparative Evaluation for Spatial Prediction in log-Gaussian Cox Processes

8 February 2012
Benjamin M. Taylor
P. Diggle
ArXiv (abs)PDFHTML

Papers citing "INLA or MCMC? A Tutorial and Comparative Evaluation for Spatial Prediction in log-Gaussian Cox Processes"

19 / 19 papers shown
Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process models
Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process models
Takashi Hayakawa
Satoshi Asai
119
0
0
09 Nov 2025
Exact Bayesian Gaussian Cox Processes Using Random Integral
Exact Bayesian Gaussian Cox Processes Using Random Integral
Bingjing Tang
Julia Palacios
219
0
0
28 Jun 2024
Twenty ways to estimate the Log Gaussian Cox Process model with point
  and aggregated case data: the rts2 package for R
Twenty ways to estimate the Log Gaussian Cox Process model with point and aggregated case data: the rts2 package for R
Samuel I Watson
233
1
0
14 Mar 2024
At the junction between deep learning and statistics of extremes:
  formalizing the landslide hazard definition
At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definitionJournal of Geophysical Research (JGR), 2024
Ashok Dahal
Raphael Huser
Luigi Lombardo
107
18
0
25 Jan 2024
Spatial meshing for general Bayesian multivariate models
Spatial meshing for general Bayesian multivariate modelsJournal of machine learning research (JMLR), 2022
M. Peruzzi
David B. Dunson
440
8
0
25 Jan 2022
Log-Gaussian Cox Process Modeling of Large Spatial Lightning Data using
  Spectral and Laplace Approximations
Log-Gaussian Cox Process Modeling of Large Spatial Lightning Data using Spectral and Laplace Approximations
Megan L. Gelsinger
Maryclare Griffin
David S. Matteson
J. Guinness
174
1
0
30 Nov 2021
A Statistical Introduction to Template Model Builder: A Flexible Tool
  for Spatial Modeling
A Statistical Introduction to Template Model Builder: A Flexible Tool for Spatial Modeling
Aaron Osgood-Zimmerman
J. Wakefield
189
5
0
17 Mar 2021
Scalable Inference for Space-Time Gaussian Cox Processes
Scalable Inference for Space-Time Gaussian Cox Processes
Shinichiro Shirota
Sudipto Banerjee
199
10
0
16 Feb 2018
Bayesian Computation for Log-Gaussian Cox Processes--A Comparative
  Analysis of Methods
Bayesian Computation for Log-Gaussian Cox Processes--A Comparative Analysis of MethodsJournal of Statistical Computation and Simulation (JSCS), 2017
Ming Teng
F. Nathoo
T. Johnson
176
44
0
03 Jan 2017
Inference for log Gaussian Cox processes using an approximate marginal
  posterior
Inference for log Gaussian Cox processes using an approximate marginal posterior
Shinichiro Shirota
A. Gelfand
258
7
0
30 Nov 2016
Variational Fourier features for Gaussian processes
Variational Fourier features for Gaussian processes
J. Hensman
N. Durrande
Arno Solin
VLM
417
223
0
21 Nov 2016
Adaptive Multiple Importance Sampling for Gaussian Processes
Adaptive Multiple Importance Sampling for Gaussian Processes
Xiaoyu Xiong
Václav Smídl
Maurizio Filippone
320
6
0
05 Aug 2015
Palm distributions for log Gaussian Cox processes
Palm distributions for log Gaussian Cox processes
Jean‐François Coeurjolly
Jesper Møller
R. Waagepetersen
249
16
0
15 Jun 2015
Enabling scalable stochastic gradient-based inference for Gaussian
  processes by employing the Unbiased LInear System SolvEr (ULISSE)
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
Maurizio Filippone
Raphael Engler
428
32
0
22 Jan 2015
Bayesian Inference for Gaussian Process Classifiers with Annealing and
  Pseudo-Marginal MCMC
Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMCInternational Conference on Pattern Recognition (ICPR), 2013
Maurizio Filippone
228
6
0
28 Nov 2013
Pseudo-Marginal Bayesian Inference for Gaussian Processes
Pseudo-Marginal Bayesian Inference for Gaussian ProcessesIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2013
Maurizio Filippone
Mark Girolami
428
65
0
02 Oct 2013
On Russian Roulette Estimates for Bayesian Inference with
  Doubly-Intractable Likelihoods
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods
A. Lyne
Mark Girolami
Yves F. Atchadé
Heiko Strathmann
Daniel P. Simpson
717
146
0
17 Jun 2013
Sparse Approximate Inference for Spatio-Temporal Point Process Models
Sparse Approximate Inference for Spatio-Temporal Point Process Models
Botond Cseke
A. Mangion
Tom Heskes
G. Sanguinetti
227
2
0
17 May 2013
lgcp An R Package for Inference with Spatio-Temporal Log-Gaussian Cox
  Processes
lgcp An R Package for Inference with Spatio-Temporal Log-Gaussian Cox Processes
Benjamin M. Taylor
Tilman M. Davies
B. Rowlingson
P. Diggle
284
41
0
27 Oct 2011
1
Page 1 of 1