Projects per year
Personal profile
Keywords
- Mathematics and Statistics
- applied probability theory
- Markov chain Monte Carlo methods (MCMC)
- spatial statistics
- statistics
- stochastic geometry
- stochastic simulation
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Collaborations from the last five years
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Projects
- 27 Finished
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Second-order intensity-reweighted stationary isotropic and geometric anisotropic spatial point processes, with a view to Cox processes
Møller, J. & Toftaker, H.
01/01/2011 → 01/12/2016
Project: Research
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Transforming spatial point processes into Poisson processes using random superposition
Berthelsen, K. K. & Møller, J.
01/01/2011 → 01/12/2012
Project: Research
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Second-order analysis of structured inhomogeneos spatio-temporal point processes
Møller, J. & Ghorbani, M.
01/01/2011 → 01/12/2016
Project: Research
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A sequential point process model and Bayesian inference for spatial point patterns with linear structures
01/01/2011 → 30/08/2013
Project: Research
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Fitting the grain orientation distribution of a polycrystalline material conditioned on a Laguerre tessellation
Karafiátová, I., Møller, J., Pawlas, Z., Staněk, J., Seitl, F. & Beneš, V., Jun 2023, In: Spatial Statistics. 55, 100747.Research output: Contribution to journal › Journal article › Research › peer-review
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Singular Distribution Functions for Random Variables with Stationary Digits
Cornean, H., Herbst, I. W., Møller, J., Støttrup, B. B. & Sørensen, K. S., Mar 2023, In: Methodology and Computing in Applied Probability. 25, 31.Research output: Contribution to journal › Journal article › Research › peer-review
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Stochastic routing with arrival windows
Pedersen, S. A., Yang, B., Jensen, C. S. & Møller, J., 2023, (Accepted/In press) In: ACM Transactions on Spatial Algorithms and Systems.Research output: Contribution to journal › Journal article › Research › peer-review
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Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation
Vihrs, N., Møller, J. & Gelfand, A. E., Mar 2022, In: Scandinavian Journal of Statistics. 49, 1, p. 185-210 26 p.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile2 Citations (Scopus)24 Downloads (Pure) -
Characterization of random variables with stationary digits
Cornean, H. D., Herbst, I. W., Møller, J., Sørensen, K. S. & Støttrup, B. B., 15 Dec 2022, In: Journal of Applied Probability. 59, 4, p. 931-947 17 p.Research output: Contribution to journal › Journal article › Research › peer-review
Datasets
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MCMC Computations for Bayesian Mixture Models Using Repulsive Point Processes
Beraha, M. (Creator), Argiento, R. (Creator), Møller, J. (Creator) & Guglielmi, A. (Creator), Taylor & Francis, 24 Jan 2022
DOI: 10.6084/m9.figshare.16967325.v2, https://tandf.figshare.com/articles/dataset/MCMC_computations_for_Bayesian_mixture_models_using_repulsive_point_processes/16967325/2
Dataset: Supplementary material
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MCMC Computations for Bayesian Mixture Models Using Repulsive Point Processes
Beraha, M. (Creator), Argiento, R. (Creator), Møller, J. (Creator) & Guglielmi, A. (Creator), Taylor & Francis, 2022
DOI: 10.6084/m9.figshare.16967325, https://tandf.figshare.com/articles/dataset/MCMC_computations_for_Bayesian_mixture_models_using_repulsive_point_processes/16967325
Dataset: Supplementary material
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MCMC computations for Bayesian mixture models using repulsive point processes
Beraha, M. (Creator), Argiento, R. (Creator), Møller, J. (Creator) & Guglielmi, A. (Creator), Taylor & Francis, 9 Nov 2021
DOI: 10.6084/m9.figshare.16967325.v1, https://tandf.figshare.com/articles/dataset/MCMC_computations_for_Bayesian_mixture_models_using_repulsive_point_processes/16967325/1
Dataset: Supplementary material
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The Accumulated Persistence Function, a New Useful Functional Summary Statistic for Topological Data Analysis, With a View to Brain Artery Trees and Spatial Point Process Applications
Biscio, C. (Creator) & Møller, J. (Creator), Taylor & Francis, 29 Apr 2019
DOI: 10.6084/m9.figshare.7728554.v2, https://tandf.figshare.com/articles/The_accumulated_persistence_function_a_new_useful_functional_summary_statistic_for_topological_data_analysis_with_a_view_to_brain_artery_trees_and_spatial_point_process_applications/7728554/2
Dataset: Supplementary material
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The accumulated persistence function, a new useful functional summary statistic for topological data analysis, with a view to brain artery trees and spatial point process applications
Biscio, C. (Creator) & Møller, J. (Creator), Taylor & Francis, 2019
DOI: 10.6084/m9.figshare.7728554.v1, https://tandf.figshare.com/articles/The_accumulated_persistence_function_a_new_useful_functional_summary_statistic_for_topological_data_analysis_with_a_view_to_brain_artery_trees_and_spatial_point_process_applications/7728554/1
Dataset: Supplementary material
Activities
- 1 Other
Press/Media
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Statistik afkoder regnskovens biodiversitet
Rasmus Waagepetersen & Jesper Møller
09/12/2019
3 items of Media coverage
Press/Media: Press / Media
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Ni AAU-forskere modtager millioner fra Det Frie Forskningsråd
Jens Laurids Sørensen, Jesper Jensen, Kathrine Vitus, Troels Pedersen, Morten Mattrup Smedskjær, Rafal Wisniewski, Jesper Møller, Ulrik Mathias Nyman & Elisabeth De Carvalho
19/05/2017
4 items of Media coverage
Press/Media: Press / Media
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BENEDIKTE ASK: 4 MYTER OM SKOLELUKNINGER
06/09/2010
1 item of Media coverage
Press/Media: Press / Media