Jesper Møller Department of Mathematical Sciences Aalborg University Skjernvej 4A DK-9220 Aalborg Ø Phone: (+45) 99 40 88 63 Fax: (+45) 99 40 35 48 Email: jm@math.aau.dk

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Briefly about me:

• Professor at Department of Mathematical Sciences, Aalborg University.
• D.Sc. (dr.scient.), Aalborg University, January 2000.
  Ph.D. (lic.scient.) in statistics, University of Aarhus, December 1988.
  M.Sc. (cand.scient.), majoring in statistics, minoring in mathematics, University of Aarhus, June 1984.
• Current research interests: Statistics and applied probability, particularly spatial statistics, stochastic geometry, simulation-based inference, Markov chain Monte Carlo (MCMC) methods, and perfect simulation.
• Head of   the Doctoral School in Mathematics and Physics at the International Doctoral School of Technology and Science, Aalborg University.
• Formerly, Associate Editor at the Annals of Applied Probability, at Advances in Applied Probability, the Scandinavian Journal of Statistics, and Bernoulli. Formerly, National Editor at the Scandinavian Journal of Statistics.
• Initiator and responsible for the accreditation of the Bachelor and Master study in Mathematics and Technology at Aalborg University started in September 2013.
• Elected member of   the International Statistical Institute.
• Received the Spar Nord Foundation's Research Prize, 2001 (honoured by 250,000 DKK).
• According to an Essential Science Indicators (ESI) analysis, over the last 10 years at October 2007 I was among the 1% most cited researchers in the top 50% of journals in Mathematics, and within this group of mathematicians, for the October 2007 - December 2007 update period, I achieved the highest percent increase in total citations. In 2008, Thomson Reuters therefore selected me as a Rising Star in the field of Mathematics (click here for the interview by ScienceWatch and here for the interview by Nordjyske Stiftstidende).
• Knight of the Order of the Dannebrog, 2015.
• Professorem Hospitem, Charles University, Prague, since 2020.
• Full picture: click here or here or here.

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Various information:

Publications since 1999: see below. .

All publications .

Two pages CV (2020).

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Research monographs:

Comments and corrections to "Statistical Inference and Simulation for Spatial Point Processes": click here.

2024:

• J. Møller. Coupling results and Markovian structures for number representations of continuous random variables. Available at arXiv:2404.09525.
• I.W. Herbst, J. Møller and A.M. Svane. The asymptotic distribution of the scaled remainder for pseudo golden ratio expansions of a continuous random variable. Available at arXiv:2404.08387.
• I.W. Herbst, J. Møller and A.M. Svane. How many digits are needed? Methodology and Computing in Applied Probability, 26, article no. 5. Available at arXiv:2307.06685.
• J. Møller and J.G. Rasmussen. Cox processes driven by transformed Gaussian processes on linear networks - A review and new contributions. Scandinavian Journal of Statistics, 51, 1288-1322. Available at arXiv:2212.08402.

2023:

• I.W. Herbst, J. Møller and A.M. Svane (2023). The asymptotic distribution of the remainder in a certain base-$\beta$ expansion. Available at arXiv:2312.09652.
• H. Cornean. I.W. Herbst, J. Møller, B. Støttrup and K.S. Sørensen. Singular distribution functions for random variables with stationary digits. Methodology and Computing in Applied Probability, 25, article no. 31. Available at arXiv:2201.01521.
• I. Karafiátová, J. Møller, Z. Pawlas, J. Staněk, F. Seitl and V. Beneš. Fitting the grain orientation distribution of a polycrystalline material conditioned on a Laguerre tessellation. Spatial Statistics, 55, 16 pages (100747). Available at arXiv:2211.11536.
• S.Aa. Pedersen, B. Yang, C.S. Jensen and J. Møller. Stochastic routing with arrival windows. ACM Transactions on Spatial Algorithms and Systems, 9, issue 4, article no. 30, 1-48. DOI:10.1145/3617500.

2022:

• J. Møller and J.G. Rasmussen. Cox processes driven by transformed Gaussian processes on linear networks. Submitted to Scandinavian Journal of Statistics. Available at arXiv:2212.08402.
• S.Aa. Pedersen, B. Yang, C.S. Jensen and J. Møller. Stochastic routing with arrival windows. Conditionally accepted for ACM Transactions on Spatial Algorithms and Systems.
• J. Møller and N. Vihrs. Determinantal shot noise Cox processes. STAT, 611:e502 (14 pages). DOI: 10.1002/sta4.502. Available at arXiv:2112.04204.
• F. Seitl, J. Møller and V. Beneš. Fitting three-dimensional Laguerre tessellations by hierarchical marked point process models. Spatial Statistics, 51, 14 pages (100658). Available at arXiv:2110.07485.
• J. Møller and N. Vihrs. Should we condition on the number of points when modelling spatial point patterns? International Statistical Review, 90, 551–562. Available at arXiv:2108.10051.
• N. Vihrs, J. Møller and A. Gelfand. Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation. Scandinavian Journal of Statistics, 49, 185-211. Available at arXiv:2003.10490.
• H. Cornean. I.W. Herbst, J. Møller, B. Støttrup and K.S. Sørensen. Characterization of random variables with stationary digits. Journal of Applied Probability, 59, 931-947. Available at arXiv:2001.08492.
• M. Beraha, R. Argiento, J. Møller and A. Guglielmi. MCMC computations for Bayesian mixture models using repulsive point processes. Journal of Computational and Graphical Statistics, 31, 422-435. Available at arXiv:2011.06444.
• N.Y. Larsen, N. Vihrs, J. Møller, J. Sporring, X. Tan, X. Li, G. Ji, G. Rajkowska, S. Fei and J.R. Nyengaard. Layer III pyramidal cells in the prefrontal cortex reveal morphological changes in subjects with depression, schizophrenia, and suicide. Translational Psychiatry, 12, 363, 09.2022.

2021:

• J. Møller and N. Vihrs. Determinantal shot noise Cox processes. Available at arXiv:2112.04204. Submitted for journal publication.
• C.A.N. Biscio and J. Møller. Discussion of `Event history and topological data analysis´. Biometrika, 108, 779-783.
• J. Møller and E. O'Reilly. Couplings for determinantal point processes and their reduced Palm distributions with a view to quantifying repulsiveness. Journal of Applied Probability, 58, 469-483. Available at arXiv:1806.07347.
• N.Y. Larsen, X. Li, X. Tan, G. Ji, J. Lin, G. Rajkowska, J. Møller, N. Vihrs, J. Sporring, S. Fei and J.R. Nyengaard. Cellular 3D-reconstruction and analysis in the human cerebral cortex using automatic serial sections. Communications Biology, 4, 1030. https://doi.org/10.1038/s42003-021-02548-6.
• T. Shaw, J. Møller and R.P. Waagepetersen. Globally intensity-reweighted estimators for K- and pair correlation functions. Australian and New Zealand Journal of Statistics, 63, 93-118. Available at arXiv:2004.00527.
• A.D. Christoffersen, J. Møller and H.S. Christensen. Modelling columnarity of pyramidal cells in the human cerebral cortex. Australian and New Zealand Journal of Statistics, 63, 33-54. Available at arXiv:1908.05065.
• J. Møller, H.S. Christensen, F. Cuevas-Pacheco and A.D. Christoffersen. Structured space-sphere point processes and K-Functions. Methodology and Computing in Applied Probability, 23, 569-591. Available at arXiv:1812.08986.

2020:

• H.S. Christensen and J. Møller. Modelling spine locations on dendrite trees using inhomogeneous Cox point processes. Spatial Statistics, 39, Article 100478. Available at arXiv:1907.12283.
• H. Cornean. I.W. Herbst, J. Møller, B. Støttrup and K.S. Sørensen. Transfer of stationary stochastic processes to the unit interval. Part I: Characterization results. Available at arXiv:2001.08492. Submitted for journal publication.
• E. Anderes, J. Møller and J.G. Rasmussen. Isotropic covariance functions on graphs and their edges. Annals of Statistics, 48, 2478-2503. Available at arXiv:1710.01295.

2019:

• A.D. Christoffersen, J. Møller and H.S. Christensen. Modelling columnarity of pyramidal cells in the human cerebral cortex. Research Report 8, 2019, Centre for Stochastic Geometry and Advanced Bioimaging. Available at arXiv:1908.05065.
• H.S. Christensen and J. Møller. Modelling spine locations on dendrite trees using inhomogeneous Cox point processes. Research Report 6, 2019, Centre for Stochastic Geometry and Advanced Bioimaging. Available at arXiv:1907.12283.
• C.A.N. Biscio and J. Møller. 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. Journal of Computational and Graphical Statistics, 28, 671-681. Available at arXiv:1611.00630.
• J. Møller, H.S. Christensen, F. Cuevas-Pacheco and A.D. Christoffersen. Structured space-sphere point processes and K-functions. Methodology and Computing in Applied Probability, 23, 569-591. Available at arXiv:1812.08986.
• J. Dvořák, J. Møller, T. Mrkvička and S. Soubeyrand. Quick inference for log Gaussian Cox processes with non-stationary underlying random fields. Spatial Statistics, 33, 23 pages (100318). Available at arXiv:1903.12035.

2018:

• J. Møller and E. O'Reilly. Couplings for determinantal point processes and their reduced Palm distributions with a view to quantifying repulsiveness. Submitted for journal publication. Research Report 7, 2018, Centre for Stochastic Geometry and Advanced Bioimaging. Available at arXiv:1806.07347.
• F. Cuevas-Pacheco and J. Møller. Log Gaussian Cox processes on the sphere. Spatial Statistics, 26, 69-82. Available at arXiv:1803.03051.
• J. Møller and A.D. Christoffersen. Pair correlation functions and limiting distributions of iterated cluster point processes. Journal of Applied Probability, 55, 789-809. DOI:10.1017/jpr.2018.50. Available at arXiv:1711.08984.
• R.D. Jacobsen, J. Møller, M. Nielsen and M.G. Rasmussen. Investigations of the effects of random sampling patterns on the stability of generalized sampling. Applied and Computational Harmonic Analysis, 45, 453-461. Available at arXiv:1607.04424.
• J. Møller, M. Nielsen, E. Porcu and E. Rubak. Determinantal point process models on the sphere. Bernoulli, 24, 1171-1201 (download: click here). DOI:10.3150/16-BEJ896. Available at arXiv:1607.03675.

2017:

• J. Møller and A.D. Christoffersen. Pair correlation functions and limiting distributions of iterated cluster point processes. Research Report 11, 2017, Centre for Stochastic Geometry and Advanced Bioimaging. Available at arXiv:1711.08984.
• R.D. Jacobsen and J. Møller. Frequentist and Bayesian inference for Gaussian–log-Gaussian wavelet trees and statistical signal processing applications. STAT, 6, 248–256. DOI:10.1002/sta4.156. Available at arXiv:1405.0379.
• E. Anderes, J. Møller and J.G. Rasmussen. Isotropic covariance functions on graphs and their edges. Submitted for journal publication. Research Report 10, 2017, Centre for Stochastic Geometry and Advanced Bioimaging. Available at arXiv:1710.01295.
• J.-F. Coeurjolly, J. Møller and R. Waagepetersen. Palm distributions for log Gaussian Cox processes. Scandinavian Journal of Statistics, 44, 192–203. DOI:10.1111/sjos.12248. Available at arXiv:1506.04576.
• J.-F. Coeurjolly, J. Møller and R. Waagepetersen. A tutorial on Palm distributions for spatial point processes. International Statistical Review, 85, 404-420. Available at arXiv:1512.05871.
• J. Møller and R. Waagepetersen. Some recent developments in statistics for spatial point patterns. Annual Review of Statistics and Its Applications, 4, 317-342.

2016:

• J. Møller, F. Safavimanesh and J.G. Rasmussen. The cylindrical K-function and Poisson line cluster point processes. Biometrika, 103, 937-954. DOI:10.1093/biomet/asw044. (Download: click here.)
• C.A.N. Biscio and J. Møller. 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. Available at arXiv:1611.00630.
• A.H.Rafati, F. Safavimanesh, G. Wegener, M. Ardalan, B. Elfing, A.A. Mathe, J.B. Rasmussen, J. Møller, E.B.V. Jensen and J.R. Nyengaard. The Effect of Maternal Separation on the Neurovascular Alteration of Medial Prefrontal Cortex and Serum Level of Brain-derived Neurotrophic Factor in a Genetic Rat Model of Depression. Submitted for journal publication.
• J. Møller and E. Rubak. Functional summary statistics on the sphere with an application to determinantal point processes. Spatial Statistics, 18, 4-23. DOI:10.1016/j.spasta.2016.06.004.
• J. Møller and E. Rubak. Determinantal point processes and functional summary statistics on the sphere. Research Report 2, 2016, Centre for Stochastic Geometry and Advanced Bioimaging.
• J. Møller, M. Ghorbani and E. Rubak. Mechanistic spatio-temporal point process models for marked point processes, with a view to forest stand data. Biometrics, 72, 687-696. DOI:10.1111/biom.12466.
• F. Lavancier and J. Møller. Modelling aggregation on the large scale and regularity on the small scale in spatial point pattern datasets. Scandinavian Journal of Statistics, 43, 587-609. DOI:10.1111/sjos.12193.
• A.H. Rafati, F. Safavimanesh, K.-A. Dorph-Petersen, J.G. Rasmussen, J. Møller and J.R. Nyengaard. Detection and spatial characterization of minicolumnarity in the human cerebral cortex. Journal of Microscopy, 261, 115-126. DOI:10.1111/jmi.12321.

2015:

• J.-F. Coeurjolly, J. Møller and R. Waagepetersen. Conditioning in spatial point processes. Research Report 14, 2015, Centre for Stochastic Geometry and Advanced Bioimaging. Available at arXiv:1512.05871.
• J. Møller, M. Nielsen, E. Porcu and E. Rubak. Determinantal point process models on the sphere. Research Report 13, 2015, Centre for Stochastic Geometry and Advanced Bioimaging.
• J.-F. Coeurjolly, J. Møller and R. Waagepetersen. Palm distributions for log Gaussian Cox processes. Research Report 9, 2015, Centre for Stochastic Geometry and Advanced Bioimaging.
• J. Møller, F. Safavimanesh and J.G. Rasmussen. The cylindrical K-function and Poisson line cluster point processes. Research Report 3, 2015, Centre for Stochastic Geometry and Advanced Bioimaging. Available at arXiv:1503.07423.
• F. Lavancier, J. Møller and E. Rubak. Determinantal point process models and statistical inference. Journal of Royal Statistical Society: Series B (Statistical Methodology), 77, 853-877. Doi 10.1111/rssb.12096.
• V. Suryaprakash, J. Møller and G.P. Fettweis. On the modeling and analysis of heterogeneous radio access networks using a Poisson cluster process. IEEE Transactions on Wireless Communications, 14, 1035-1047. DOI 10.1109/TWC.2014.2363454.
• J. Møller and J.G. Rasmussen. Spatial cluster point processes related to Poisson-Voronoi tessellations. Stochastic Environmental Research and Risk Assessment, 29, 431-441. DOI 10.1007/s00477-014-0914-3.
• R. Fierro, V. Leiva and J. Møller. The Hawkes process with different excitation functions and its asymptotic behaviour. Journal in Applied Probability, 52, 37-54. DOI 10.1017/S0021900200012183.
• J. Møller and M. Ghorbani. Functional summary statistics for the Johnson-Mehl model. Journal of Statistical Computation and Simulation, 85, 899-916. DOI 10.1080/00949655.2013.850691.
• J. Møller and D. Stoyan. Stochastic geometry and random tessellations. To appear in Tessellations in the Sciences: Virtues, Techniques and Applications of Geometric Tilings, Eds. R. van de Weijgaert, G. Vegter, V. Icke and J. Ritzerveld, Springer-Verlag.

2014:

• F. Lavancier, J. Møller and E. Rubak. Determinantal point process models and statistical inference: Extended version. Available at arXiv:1205.4818.
• J. Møller, M. Ghorbani and E. Rubak. Mechanistic spatio-temporal point process models for marked point processes, with a view to forest stand data. Research Report R-2014-07, Department of Mathematical Sciences, Aalborg University.
• P.G.M. Forbes, S. Lauritzen and J. Møller. Fingerprint analysis with marked point processes. Research Report R-2014-06, Department of Mathematical Sciences, Aalborg University. Submitted for journal publication.
• J. Møller and R.D. Jacobsen. Gaussian-log-Gaussian wavelet trees, frequentist and Bayesian inference, and statistical signal processing applications. Research Report R-2014-04, Department of Mathematical Sciences, Aalborg University.
• T.S. Nielsen, J.R. Nyengaard, J. Møller, J. Banner, L.P. Nielsen and U.T. Baandrup. Quantitative diagnosis of lymphocytic myocarditis in forensic medicine. Forensic Science International, 238, 9-15. DOI:10.1016/j.foresciint.2014.02.012.
• J. Møller and H. Toftager. Geometric anisotropic spatial point pattern analysis and Cox processes. Scandinavian Journal of Statistics, 41, 414-435. DOI 10.1111/sjos.12041.
• J.-F. Coeurjolly and J. Møller. Variational approach for spatial point process intensity estimation. Bernoulli, 20, 1097-1125. DOI:10.3150/13-BEJ516.

2013:

• R. Fierro, V. Leiva and J. Møller. The Hawkes process with different excitation functions and its asymptotic behaviour. Research Report R-2013-14, Department of Mathematical Sciences, Aalborg University.
• J. Møller and J.G. Rasmussen. Spatial cluster point processes related to Poisson-Voronoi tessellations. Research Report R-2013-10, Department of Mathematical Sciences, Aalborg University.
• J. Møller and M. Ghorbani. Functional summary statistics for the Johnson-Mehl model. Research Report R-2013-01, Department of Mathematical Sciences, Aalborg University.

2012:

• J.-F. Coeurjolly and J. Møller. Variational approach for spatial point process intensity estimation. Research Report R-2012-04, Department of Mathematical Sciences, Aalborg University.
• F. Lavancier, J. Møller and E. Rubak. Statistical aspects of determinantal point processes. Research Report R-2012-02, Department of Mathematical Sciences, Aalborg University.
• J. Møller. Contribution to the discussion of Fearnhead, P., and Prangle, D. (2012): Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation. Journal of Royal Statistical Society: Series B (Statistical Methodology), 74, 465.
• J. Møller and M. Ghorbani. Aspects of second-order analysis of structured inhomogeneous spatio-temporal point processes. Statistica Neerlandica, 66, 472-491.
• J. Møller and K.K. Berthelsen. Transforming spatial point processes into Poisson processes using random superposition. Advances in Applied Probability, 44, 42-64.
• J. Møller and J.G. Rasmussen. A sequential point process model and Bayesian inference for spatial point patterns with linear structures. Scandinavian Journal of Statistics, 39, 618-634.
• J. Møller and H. Toftager. Geometric anisotropic spatial point pattern analysis and Cox processes. Research Report R-2012-01, Department of Mathematical Sciences, Aalborg University.

2011:

• A. Baddeley, E. Rubak and J. Møller. Score, pseudo-score and residual diagnostics for goodness-of-fit of spatial point process models. Statistical Science, 26, 613-646.
• J. Møller. Memorial: Julian Besag 1945-2010. See also the booklet (save it as a link).
• J. Møller. Contribution to the discussion of Lindgren, F., Rue, H. and Lindström, J. (2011): An explicit link between Gaussian fields and Gaussian Markov random fields: The stochastic partial differential equation approach. Journal of Royal Statistical Society: Series B (Statistical Methodology), 73, 466-467.
• M.L. Yiu, C.S. Jensen, J. Møller and H. Lu. Design and Analysis of a Ranking Approach to Private Location-Based Services. ACM Transactions on Database Systems (TODS), 36(2): Article 10, 42 pages.
• J. Møller and M.L. Yiu. Probabilistic results for a mobile service scenario. Advances in Applied Probability, 43, 322-334.

2010:

• J. Møller and M. Ghorbani. Second-order analysis of structured inhomogeneous spatio-temporal point processes. Research Report R-2010-11, Department of Mathematical Sciences, Aalborg University.
• E. Rubak, J. Møller and P. McCullagh. Statistical inference for a class of multivariate negative binomial distributions. Research Report R-2010-10, Department of Mathematical Sciences, Aalborg University. Submitted for journal publication.
• J. Møller and K.K. Berthelsen. Transforming spatial point processes into Poisson processes using random superposition. Research Report R-2010-09, Department of Mathematical Sciences, Aalborg University.
• J. Møller and J.G. Rasmussen. A sequential point process model and Bayesian inference for spatial point patterns with linear structures. Research Report R-2010-08, Department of Mathematical Sciences, Aalborg University.
• A. Baddeley, E. Rubak and J. Møller. Score, pseudo-score and residual diagnostics for goodness-of-fit of spatial point process models. Research Report R-2010-06, Department of Mathematical Sciences, Aalborg University.
• J. Møller and M.L. Yiu. Probabilistic results for a mobile service scenario. Research Report R-2010-03, Department of Mathematical Sciences, Aalborg University.
• J. Møller, M. L. Huber and R. L. Wolpert. Perfect simulation and moment properties for the Matérn type III process. Stochastic Processes and their Applications, 120, 2142-2158.
• Brian H. Aukema, Jun Zhu, Jesper Møller, Jakob G. Rasmussen, and Kenneth F. Raffa. Predisposition to bark beetle attack by root herbivores and associated pathogens: Roles in forest decline, gap formation, and persistence of endemic bark beetle populations. Forest Ecology and Management, 259, 374-382.
• J. Møller. Inference. Chapter 9 in New Perspectives in Stochastic Geometry, Eds. W.S. Kendall and I. Molchanov, Oxford University Press, Oxford, 307-347.
• J. Møller and R.P. Schoenberg. Thinning spatial point processes into Poisson processes. Advances in Applied Probability, 42, 347-358.
• J. Møller. Parametric methods. Chapter 19 in A Handbook of Spatial Statistics. Eds. A.E. Gelfand, P. Diggle, M. Fuentes, and P. Guttorp, Chapman and Hall/CRC Press, 317-337.
• J. Møller and Carlos Diaz-Avalos. Structured spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires. Scandinavian Journal of Statistics, 37, 2-25.
• J. Møller and K. Helisova. Likelihood inference for unions of interacting discs. Scandinavian Journal of Statistics, 37, 365-381.
• J. Møller and Ege Rubak. A model for positively correlated count variables. International Statistical Review, 78, 65-80.

2009:

• J. Møller, M. L. Huber and R. L. Wolpert. Perfect simulation and moment properties for the Matérn type III process. Research Report R-2009-12, Department of Mathematical Sciences, Aalborg University.
• J. Møller and M.L. Yiu. The distribution of communication cost for a mobile service scenario. Research Report R-2009-11, Department of Mathematical Sciences, Aalborg University.
• J. Møller and K. Helisova. Simulation of random set models for unions of discs and the use of power tessellations. The Sixth International Symposium on Voronoi Diagrams in Science and Engineering, Ed. F. Anton, IEEE Computer Socity, Los Alamitos, California, 99-108.
• K. Helisova and J. Møller. Model pro nahodne sjednoceni kruhu se vzajemnymi interakcemi. Proceedings of the ROBUST '08 conference, Eds. J. Antoch and G. Dohnal, JCMF, Prague, 89-96.
• K. Helisova and J. Møller. Model for random union of interacting discs. Stereology and Image Analysis. Ecs10: Proceedings of The 10th European Congress of ISS, Eds. V. Capasso et al., The MIRIAM Project Series, Vol. 4, ESCULAPIO Pub. Co., Bologna, Italy, 437-441.
• J. Møller and R.P. Schoenberg. Thinning spatial point processes into Poisson processes. Research Report R-2009-01, Department of Mathematical Sciences, Aalborg University.
• J. Møller and J.G. Rasmussen. Modelling point patterns with linear structures. Stereology and Image Analysis. Ecs10: Proceedings of The 10th European Congress of ISS, Eds. V. Capasso et al., The MIRIAM Project Series, Vol. 4, ESCULAPIO Pub. Co., Bologna, Italy, 273-278.
• J.B. Illian, J. Møller and R.P. Waagepetersen. Hierarchical spatial point process analysis for a plant community with high biodiversity. Environmental and Ecological Statistics, 16, 389-405.

2008:

• J. Møller and K. Helisova. Likelihood inference for unions of interacting discs. Research Report R-2008-18, Department of Mathematical Sciences, Aalborg University.
• J. Møller and Ege Rubak. Properties and simulation of alpha-permanental random fields. Research Report R-2008-13, Department of Mathematical Sciences, Aalborg University.
• J. Møller and Carlos Diaz-Avalos. Structured spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires. Research Report R-2008-07, Department of Mathematical Sciences, Aalborg University.
• J. Møller. Parametric methods for spatial point processes. Research Report R-2008-04, Department of Mathematical Sciences, Aalborg University.
• J. Møller and K. Helisova. Power diagrams and interaction processes for unions of discs. Advances in Applied Probility, 40, 321-347.
• K.K. Berthelsen and J. Møller. Non-parametric Bayesian inference for inhomogeneous Markov point processes. Australian and New Zealand Journal of Statistics, 50, 257-272.
• J. Zhu, J.G. Rasmussen, J. Møller, B.H. Aukema and K.F. Raffa. Spatial-temporal modeling of forest gabs generated by colonization from below- and above-ground bark beetle species. Journal of the American Statistical Association, 103, 162-177.
• A. Baddeley, J. Møller and A.G. Pakes. Properties of residuals for spatial point processes. Annals of the Institute of Statistical Mathematics, 60, 627-649.
• J. Møller. Contribution to the discussion of P. McCullagh (2008): Sampling bias and logistic models. Journal of Royal Statistical Society: Series B (Statistical Methodology), 70, 669.

2007:

• J. Møller. Inference. Research Report R-2007-22, Department of Mathematical Sciences, Aalborg University.
• J. Møller and D. Stoyan. Stochastic geometry and random tessellations. Research Report R-2007-28, Department of Mathematical Sciences, Aalborg University.
• J. Møller and K. Helisova. Power diagrams and interaction processes for unions of discs. Research Report R-2007-15, Department of Mathematical Sciences, Aalborg University.
• J.G. Rasmussen, J. Møller, B.H. Aukema, K.F. Raffa and J. Zhu. Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times. Journal of Royal Statistical Society: Series B (Statistical Methodology), 69, 701-713.
• J. Møller and R.P. Waagepetersen. Modern statistics for spatial point processes (with discussion). Scandinavian Journal of Statistics, 34, 643-711.
• J. Møller and G.L. Torrisi. The pair correlation function of spatial Hawkes processes. Statistics and Probability Letters, 77, 995-1003.
• J. Møller and K. Mengersen. Ergodic averages for monotone functions via dominating processes. Bayesian Statistics 8, Eds. J.M. Bernardo, M.J. Bayarri, J.O. Berger, A.P. Dawid, D. Heckerman, A.F.M. Smith and M. West, Oxford University Press, 643-648.
• J. Møller and K. Mengersen. Ergodic averages using upper and lower dominating processes. Bayesian Analysis, 2, 761-782.
• Ø. Skare, J. Møller and E.B.V. Jensen. Bayesian analysis of spatial point processes in the neighbourhood of Voronoi networks. Statistics and Computing, 17, 369-379.

2006:

• P. McCullagh and J. Møller. The permanental process. Advances in Applied Probability, 38, 873-888.
• J.G. Rasmussen, J. Møller, B.H. Aukema, K.F. Raffa and J. Zhu. Bayesian inference for multivariate point processes observed at sparsely distributed times. Research Report R-2006-24, Department of Mathematical Sciences, Aalborg University.
• J. Møller and R.P. Waagepetersen. Modern statistics for spatial point processes. Research Report R-2006-12, Department of Mathematical Sciences, Aalborg University.
• J.B. Illian, J. Møller and R.P. Waagepetersen. Spatial point process analysis for a plant community with high biodiversity. Research Report R-2006-05, Department of Mathematical Sciences, Aalborg University.
• J. Zhu, J.G. Rasmussen, J. Møller, B.H. Aukema and K.F. Raffa. Spatial-temporal modeling of forest gabs generated by colonization from below- and above-ground bark beetle species. Research Report R-2006-04, Department of Mathematical Sciences, Aalborg University.
• A. Baddeley, J. Møller and A.G. Pakes. Properties of residuals for spatial point processes. Research Report R-2006-03, Department of Mathematical Sciences, Aalborg University.
• Ø. Skare, J. Møller and E.B.V. Jensen. Bayesian analysis of spatial point processes in the neighbourhood of Voronoi networks. Research Report R-2006-02, Department of Mathematical Sciences, Aalborg University.
• J. Møller and K. Mengersen. Ergodic averages for monotone functions using upper and lower dominating processes. Research Report R-2006-01, Department of Mathematical Sciences, Aalborg University.
• J. Møller, A.N. Pettitt, K.K. Berthelsen and R.W. Reeves. An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika, 93, 451-458. (Download: click here.)
• J. Møller and J.G. Rasmussen. Approximate simulation of Hawkes processes. Methodology and Computing in Applied Probability, 8, 53-64.
• J. Møller. Contribution to the discussion of A. Beskos, O. Papaspiliopoulos, G.O. Roberts and P. Fearnhead (2006): Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes. Journal of Royal Statistical Society: Series B (Statistical Methodology), 68, 373.
• K.K. Berthelsen and J. Møller. Bayesian analysis of Markov point processes. In Case Studies in Spatial Point Process Modeling (Eds. A. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan), Springer Lecture Notes in Statistics 185, Springer-Verlag, New York, 85-97.

2005:

• J. Møller. Properties of spatial Cox process models. Journal of Statistical Research of Iran, 2, 1-18.
• V. Benes, K. Bodlak, J. Møller and R.P. Waagepetersen. A case study on point process modelling in disease mapping. Image Analysis and Stereology, 24, 159-168.
• P. McCullagh and J. Møller. The permanent process. Research Report R-2005-29, Department of Mathematical Sciences, Aalborg University.
• J. Møller and G.L. Torrisi. Second order analysis for spatial Hawkes processes. Research Report R-2005-20, Department of Mathematical Sciences, Aalborg University.
• A. Baddeley, R. Turner, J. Møller and M. Hazelton. Residual analysis for spatial point processes (with discussion). Journal of Royal Statistical Society: Series B (Statistical Methodology), 67, 617-666.
• J. Møller and J.G. Rasmussen. Perfect simulation of Hawkes processes. Advances in Applied Probability, 37, 629-646.
• J. Møller and G.L. Torrisi. Generalised shot noise Cox processes. Advances in Applied Probability, 37, 48-74.

2004:

• J. Møller and R.P. Waagepetersen. Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC, Boca Raton.
• J. Møller and J.G. Rasmussen. Approximate simulation of Hawkes processes. Research Report R-2004-28, Department of Mathematical Sciences, Aalborg University.
• K.K. Berthelsen and J. Møller. Bayesian analysis of Markov point processes. Research Report R-2004-25, Department of Mathematical Sciences, Aalborg University.
• G. Döge, K. Mecke, J. Møller, D. Stoyan and R.P. Waagepetersen. Grand canonical simulations of hard-disk systems by simulated tempering. International Journal of Modern Physics C, 15, 129-147.
• K.K. Berthelsen and J. Møller. A short diversion into the theory of Markov chains, with a view to Markov chain Monte Carlo methods. Internal Report R-2004-01, Department of Mathematical Sciences, Aalborg University.
• A. Baddeley, R. Turner, J. Møller and M. Hazelton. Residual analysis for spatial point processes. Research Report 2004/08, School of Mathematics and Statistics, University of Western Australia. Version submitted for JRSS B.
• J. Møller and G.L. Torrisi. Generalised shot noise Cox processes. Research Report R-2004-07, Department of Mathematical Sciences, Aalborg University.
• J. Møller and J.G. Rasmussen. A note on a perfect simulation algorithm for marked Hawkes processes. In Spatial point process modelling and its applications. Eds. A. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan, Publicacions de la Universitat Jaume I, 187-192.
• K.K. Berthelsen and J. Møller. A Bayesian MCMC method for point process models with intractable normalising constants. In Spatial point process modelling and its applications (Eds. A. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan), Publicacions de la Universitat Jaume I, 7-15.
• J. Møller, A.N. Pettitt, K.K. Berthelsen and R.W. Reeves. An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Research Report R-2004-02, Department of Mathematical Sciences, Aalborg University.

2003:

• K.K. Berthelsen and J. Møller. Likelihood and non-parametric Bayesian MCMC inference for spatial point processes based on perfect simulation and path sampling. Scandinavian Journal of Statistics, 30, 549-564.
• J. Møller. Shot noise Cox processes. Advances in Applied Probability, 35, 614-640.
• V. Benes, K. Bodlak, J. Møller and R. Waagepetersen. Application of log Gaussian Cox processes in disease mapping. Proceedings of the ISI Conference on Environmental Statistics and Health, Santiago de Compostela, 2003. Eds. J. Mateu, D. Holland and W. Gonzalez-Manetiga, 95-105.
• J. Møller. A comparison of spatial point process models in epidemiological aplications. Highly Structured Stochastic Systems, Eds. P.J. Green, N.L. Hjort and S. Richardson, Oxford University Press, 264-268.
• J. Møller (editor). Spatial Statistics and Computational Methods, Lecture Notes in Statistics 173, Springer-Verlag, New York.
• J. Møller and R.P. Waagepetersen. An introduction to simulation-based inference for spatial point processes. In J. Møller, Spatial Statistics and Computational Methods, Lecture Notes in Statistics 173, Springer-Verlag, New York, 143-198.
• J. Møller. Contribution to the discussion of S.P. Brooks, P. Giudici and G.O. Roberts: Efficient constructions of reversible jump Markov chain Monte Carlo proposal distributions. Journal of Royal Statistical Society: Series B (Statistical Methodology), 65, 42-43.
• P.G. Blackwell and J. Møller. Bayesian analysis of deformed tessellation models. Advances in Applied Probability, 35, 4-26.

2002:

• K.K. Berthelsen and J. Møller. A primer on perfect simulation for spatial point processes. Bulletin of the Brazilian Mathematical Society, 33, 351-367.
• J. Møller and R.P. Waagepetersen. Statistical inference for Cox processes. Spatial Cluster Modelling, Eds. Andrew B. Lawson and David Denison, Chapman and Hall/CRC, 37-60.
• V. Benes, K. Bodlak, J. Møller and R.P. Waagepetersen. Bayesian analysis of log Gaussian Cox process models for disease mapping. Research Report R-02-2001, Department of Mathematical Sciences, Aalborg University.
• K.K. Berthelsen and J. Møller. Spatial jump processes and perfect simulation. Morphology of Condensed Matter, Eds. K. Mecke and D. Stoyan, Lecture Notes in Physics, Vol. 600, Springer-Verlag, 391-417.
• M.B. Hansen, J. Møller and F.Aa. Tøgersen. Bayesian contour detection in a time series of ultrasound images through dynamic deformable template models. Biostatistics, 3, 213-228.
• A. Mira, J. Møller and G.O. Roberts. Corrigendum: perfect slice samplers. Journal of Royal Statistical Society: Series B (Statistical Methodology), 64, 581.

2001:

• S. Mase, J. Møller, D. Stoyan, R.P. Waagepetersen and G. Döge. Packing densities and simulated tempering for hard core Gibbs point processes. Annals of the Institute of Statistical Mathematics, 53, 661-680.
• J. Møller. A review of perfect simulation in stochastic geometry. Selected Proceedings of the Symposium on Inference for Stochastic Processes, Eds. I.V. Basawa, C.C. Heyde and R.L. Taylor, IMS Lecture Notes & Monographs Series, 2001, Volume 37, pages 333-355.
• A. Brix and J. Møller. Space-time multi type log Gaussian Cox processes with a view to modelling weed data. Scandinavian Journal of Statistics, 28, 471-488.
• O.F. Christensen, J. Møller and R.P. Waagepetersen. Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models. Methodology and Computating in Applied Probability, 3, 309-327.
• A. Mira, J. Møller and G.O. Roberts. Perfect slice samplers. Journal of Royal Statistical Society: Series B (Statistical Methodology), 63, 593-606.
• A. Mira, J. Møller and G. O. Roberts. Perfect simple slice samplers. In Bulletin of the International Statistical Institute, 53rd Session Proceedings}, Tome LIX, Book 1, 73-79.
• J. Møller and Ø. Skare. Coloured Voronoi tessellations for Bayesian image analysis and reservoir modelling. Statistical Modelling, 1, 213-232.

2000:

• A. Baddeley, J. Møller and R. Waagepetersen. Non- and semi-parametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica, 54, 329-350.
• O.F. Christensen, J. Møller and R.P. Waagepetersen. Analysis of spatial data using generalized linear mixed models and Langevin-type Markov chain Monte Carlo. Research Report R-00-2009, Department of Mathematical Sciences, Aalborg University.
• G. Döge, K. Mecke, J. Møller, D. Stoyan and R.P. Waagepetersen. Grand canonical simulations of hard-disk systems by simulated tempering. Research Report R-00-2003, Department of Mathematical Sciences, Aalborg University.
• W.S. Kendall and J. Møller. Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes. Advances in Applied Probability, 32, 844-865.
• J. Møller. Aspects of Spatial Statistics, Stochastic Geometry and Markov Chain Monte Carlo, D.Sc. thesis accepted by the Faculty of Engineering and Science, Aalborg University.

1999:

• O. Häggström, M.N.M. van Lieshout and J. Møller. Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes. Bernoulli, 5, 641-659.
• W.S. Kendall and J. Møller. Perfect Metropolis-Hastings simulation of locally stable point processes. Research Report R-99-2001, Department of Mathematical Sciences, Aalborg University.
• W.S. Kendall and J. Møller. Perfect implementation of a Metropolis-Hastings simulation of Markov point processes. Research Report 348, Department of Statistics, University of Warwick.
• J. Møller. Notes on Markov chain Monte Carlo methods. Lecture Notes, Department of Mathematical Sciences, Aalborg University.
• J. Møller. Markov chain Monte Carlo and spatial point processes. In Stochastic Geometry: Likelihood and Computations, Eds. O.E. Barndorff-Nielsen, W.S. Kendall and M.N.M. van Lieshout, Monographs on Statistics and Applied Probability, Boca Raton, Chapman and Hall/CRC, 141-172.
• J. Møller. Topics in Voronoi and Johnson-Mehl tessellations. In Stochastic Geometry: Likelihood and Computations, Eds. O.E. Barndorff-Nielsen, W.S. Kendall and M.N.M. van Lieshout, Monographs on Statistics and Applied Probability, Boca Raton, Chapman and Hall/CRC, 173-198.
• J. Møller. Perfect simulation of conditionally specified models. Journal of Royal Statistical Society: Series B (Statistical Methodology), 61, 251-264.
• J. Møller and G. Nicholls. Perfect simulation for sample-based inference. Research Report R-99-2011, Department of Mathematical Sciences, Aalborg University. To appear in Statistics and Computing (conditionally accepted).
• J. Møller and R.P. Waagepetersen. Contribution to the discussion of Besag, J. and Higdon, D. (1999): Bayesian analysis of agricultural field experiments. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61, 735.
• J. Møller and K. Schladitz. Extensions of Fill's algorithm for perfect simulation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61, 955-969.

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Last modified: Sun Feb 01 14:37:28 Romance Standard Time 2009