Jesper Møller Department of Mathematical Sciences Aalborg University Thomas Manns Vej 23, Office 1.342 DK-9220 Aalborg Ø Phone: (+45) 99 40 88 63 Fax: (+45) 99 40 35 48 Email: jm@math.aau.dk

• Professor at Department of Mathematical Sciences, Aalborg University until August 31 2026. Thereafter Professor Emeritus.
• 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.
• Research interests: Mathematical statistics and applied probability, particularly spatial statistics, stochastic geometry, simulation-based inference, Markov chain Monte Carlo (MCMC) methods, perfect simulation, harmonic analysis and ergodic number theory combined with statistical inference.
• Formerly, 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.
• Knight of the Order of the Dannebrog, 2015.
• Professorem Hospitem, Charles University, Prague, 2020.
• Full picture: click here or here or here.

Theses

• J. Møller (2000). Aspects of Spatial Statistics, Stochastic Geometry and Markov Chain Monte Carlo, D.Sc. thesis accepted by the Faculty of Engineering and Science, Aalborg University.
• J. Møller (1988). Stochastic Geometry and Markov Models. Ph.D. thesis, Department of Theoretical Statistics, University of Aarhus.
• J. Møller (1984). Normale transformationsmodeller. Statistiske Interna 39, Department of Theoretical Statistics, University of Aarhus. (M.Sc. thesis.)

Monographs

• J. Møller and R.P. Waagepetersen (2004). Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC, Boca Raton.
• J. Møller (2003), editor. Spatial Statistics and Computational Methods, Lecture Notes in Statistics 173, Springer-Verlag, New York.
• J. Møller (1994). Lectures on Random Voronoi Tessellations. Lecture Notes in Statistics 87, Springer-Verlag, New York.
  
  
  
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Peer-reviewed journal articles

• M. Beraha and J. Møller (2026). Sufficient digits and density estimation: A Bayesian nonparametric approach using generalized finite Pólya trees. To appear in Bayesian Analysis. Available at arXiv:2506.09437.
• J. Møller (2025). Coupling results and Markovian structures for number representations of continuous random variables. Journal of Applied Probability, 62, 1340-1359. Available at arXiv:2404.09525.
• I.W. Herbst, J. Møller and A.M. Svane (2025). The asymptotic distribution of the scaled remainder for pseudo golden ratio expansions of a continuous random variable. Methodology and Computing in Applied Probability, 27, article no. 10. Available at arXiv:2404.08387.
• J. Møller and J.G. Rasmussen (2024). 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.
• I.W. Herbst, J. Møller and A.M. Svane (2024). How many digits are needed? Methodology and Computing in Applied Probability, 26, article no. 5. Available at arXiv:2307.06685.
• S.Aa. Pedersen, B. Yang, C.S. Jensen and J. Møller (2023). Stochastic routing with arrival windows. ACM Transactions on Spatial Algorithms and Systems, 9, issue 4, article no. 30, 1-48. DOI:10.1145/3617500.
• I. Karafiátová, J. Møller, Z. Pawlas, J. Staněk, F. Seitl and V. Beneš (2023). 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.
• H. Cornean. I.W. Herbst, J. Møller, B. Støttrup and K.S. Sørensen (2023). Singular distribution functions for random variables with stationary digits. Methodology and Computing in Applied Probability, 25, article no. 31. Available at arXiv:2201.01521.
• J. Møller and N. Vihrs (2022). Determinantal shot noise Cox processes. STAT, 611:e502 (14 pages). DOI: 10.1002/sta4.502. Available at arXiv:2112.04204.
• N.Y. Larsen, N. Vihrs, J. Møller, J. Sporring, X. Tan, X. Li, G. Ji, G. Rajkowska, S. Fei and J.R. Nyengaard (2022). Layer III pyramidal cells in the prefrontal cortex reveal morphological changes in subjects with depression, schizophrenia, and suicide. Translational Psychiatry, 12, 363, 09.2022.
• F. Seitl, J. Møller and V. Beneš (2022). 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 (2022). Should we condition on the number of points when modelling spatial point patterns? International Statistical Review, 90, 551–562. Available at arXiv:2108.10051.
• H. Cornean. I.W. Herbst, J. Møller, B. Støttrup and K.S. Sørensen (2022). 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 (2022). 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. Vihrs, J. Møller and A. Gelfand (2022). 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.
• J. Møller and E. O'Reilly (2021). 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 (2021). 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.
• J. Møller, H.S. Christensen, F. Cuevas-Pacheco and A.D. Christoffersen (2021). Structured space-sphere point processes and K-Functions. Methodology and Computing in Applied Probability, 23, 569-591. Available at arXiv:1812.08986.
• T. Shaw, J. Møller and R.P. Waagepetersen (2021). 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 (2021). 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.
• H.S. Christensen and J. Møller (2020). Modelling spine locations on dendrite trees using inhomogeneous Cox point processes. Spatial Statistics, 39, Article 100478. Available at arXiv:1907.12283.
• E. Anderes, J. Møller and J.G. Rasmussen (2020). Isotropic covariance functions on graphs and their edges. Annals of Statistics, 48, 2478-2503. Available at arXiv:1710.01295.
• J. Dvořák, J. Møller, T. Mrkvička and S. Soubeyrand (2019). Quick inference for log Gaussian Cox processes with non-stationary underlying random fields. Spatial Statistics, 33, 23 pages (100318). Available at arXiv:1903.12035.
• C.A.N. Biscio and J. Møller (2019). 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 and A.D. Christoffersen (2018). 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 (2018). 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.
• F. Cuevas-Pacheco and J. Møller (2018). Log Gaussian Cox processes on the sphere. Spatial Statistics, 26, 69-82. Available at arXiv:1803.03051.
• J. Møller, M. Nielsen, E. Porcu and E. Rubak (2018). Determinantal point process models on the sphere. Bernoulli, 24, 1171-1201 (download: click here). DOI:10.3150/16-BEJ896.
• J.-F. Coeurjolly, J. Møller and R. Waagepetersen (2017). Palm distributions for log Gaussian Cox processes. Scandinavian Journal of Statistics, 44, 192–203. DOI:10.1111/sjos.12248. Available at arXiv:1506.04576.
• R.D. Jacobsen and J. Møller (2017). 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.
• J.-F. Coeurjolly, J. Møller and R. Waagepetersen (2017). A tutorial on Palm distributions for spatial point processes. International Statistical Review85, 404-420. DOI:10.1111/insr.12205. Available at arXiv:1512.05871.
• J. Møller and R. Waagepetersen (2017). Some recent developments in statistics for spatial point patterns. Annual Review of Statistics and Its Applications, 4, 317-342.
• J. Møller, F. Safavimanesh and J.G. Rasmussen (2016). The cylindrical K-function and Poisson line cluster point processes. Biometrika, 103, 937-954. DOI:10.1093/biomet/asw044. (Download: click here.)
• J. Møller and E. Rubak (2016). 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, M. Ghorbani and E. Rubak (2016). 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 (2016). 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 (2016). Detection and spatial characterization of minicolumnarity in the human cerebral cortex. Journal of Microscopy, 261, 115-126. DOI:10.1111/jmi.12321.
• V. Suryaprakash, J. Møller and G.P. Fettweis (2015). 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.
• F. Lavancier, J. Møller and E. Rubak (2015). Determinantal point process models and statistical inference. Journal of Royal Statistical Society: Series B (Statistical Methodology), 77, 853-877. Doi 10.1111/rssb.12096.
• R. Fierro, V. Leiva and J. Møller (2015). 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 (2015). 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 J.G. Rasmussen (2015). 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.
• T.S. Nielsen, J.R. Nyengaard, J. Møller, J. Banner, L.P. Nielsen and U.T. Baandrup (2014). Quantitative diagnosis of lymphocytic myocarditis in forensic medicine. Forensic Science International, 238, 9-15. DOI 10.1016/j.forsciint.2014.02.012.
• J. Møller and H. Toftager (2014). 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 (2014). Variational approach for spatial point process intensity estimation. Bernoulli, 20, 1097-1125. DOI:10.3150/13-BEJ516.
• J. Møller and M. Ghorbani (2012). Aspects of second-order analysis of structured inhomogeneous spatio-temporal point processes. Statistica Neerlandica, 66, 472-491.
• J. Møller and J.G. Rasmussen (2012). 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 K.K. Berthelsen (2012). Transforming spatial point processes into Poisson processes using random superposition. Advances in Applied Probability, 44, 42-64.
• A. Baddeley, E. Rubak and J. Møller (2011). Score, pseudo-score and residual diagnostics for goodness-of-fit of spatial point process models. Statistical Science, 26, 613-646.
• J. Møller and M.L. Yiu (2011). Probabilistic results for a mobile service scenario. Advances in Applied Probability, 43, 322-334.
• M.L. Yiu, C.S. Jensen, J. Møller and H. Lu (2011). 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, M. L. Huber and R. L. Wolpert (2010). Perfect simulation and moment properties for the Matárn type III process. Stochastic Processes and their Applications, 120, 2142-2158.
• J. Møller and R.P. Schoenberg (2010). Thinning spatial point processes into Poisson processes. Advances in Applied Probability, 42, 347-358.
• B. H. Aukema, J. Zhu, J. Møller, J. G. Rasmussen, and K. F. Raffa. (2010). 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 and Ege Rubak (2010). A model for positively correlated count variables. International Statistical Review, 78, 65-80.
• J. Møller and Carlos Diaz-Avalos (2010). 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 (2010). Likelihood inference for unions of interacting discs. Scandinavian Journal of Statistics, 37, 365-381.
• J.B. Illian, J. Møller and R.P. Waagepetersen (2009). Hierarchical spatial point process analysis for a plant community with high biodiversity. Environmental and Ecological Statistics, 16, 389-405.
• J. Møller and K. Helisova (2008). Power diagrams and interaction processes for unions of discs. Advances in Applied Probability, 40, 321-347.
• K.K. Berthelsen and J. Møller (2008). Non-parametric Bayesian inference for inhomogeneous Markov point processes. Australian and New Zealand Journal of Statistics, 50, 257-272.
• A. Baddeley, J. Møller and A.G. Pakes (2008). Properties of residuals for spatial point processes. Annals of the Institute of Statistical Mathematics, 60, 627-649.
• J. Zhu, J.G. Rasmussen, J. Møller, B.H. Aukema and K.F. Raffa (2008). 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.
• J.G. Rasmussen, J. Møller, B.H. Aukema, K.F. Raffa and J. Zhu (2007). 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 K. Mengersen (2007). Ergodic averages for monotone functions using upper and lower dominating processes. Bayesian Analysis, 2, 761-782.
• J. Møller and R.P. Waagepetersen (2007). Modern statistics for spatial point processes (with discussion). Scandinavian Journal of Statistics, 34, 643-711.
• Ø. Skare, J. Møller and E.B.V. Jensen (2007). Bayesian analysis of spatial point processes in the neighbourhood of Voronoi networks. Statistics and Computing, 17, 369-379.
• J. Møller and G.L. Torrisi (2007). The pair correlation function of spatial Hawkes processes. Statistics and Probability Letters, 77, 995-1003.
• P. McCullagh and J. Møller (2006). The permanental process. Advances in Applied Probability, 38, 873-888.
• J. Møller, A.N. Pettitt, K.K. Berthelsen and R.W. Reeves (2006). An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika, 93, 451-458. (Download this by clicking here.)
• J. Møller and J.G. Rasmussen (2006). Approximate simulation of Hawkes processes. Methodology and Computing in Applied Probability, 8, 53-64.
• V. Benes, K. Bodlak, J. Møller and R.P. Waagepetersen (2005). A case study on point process modelling in disease mapping. Image Analysis and Stereology, 24, 159-168.
• J. Møller and J.G. Rasmussen (2005). Perfect simulation of Hawkes processes. Advances in Applied Probability, 37, 629-646.
• A. Baddeley, R. Turner, J. Møller and M. Hazelton (2005). Residual analysis for spatial point processes (with discussion). Journal of Royal Statistical Society: Series B (Statistical Methodology), 67, 617-666.
• J. Møller and G.L. Torrisi (2005). Generalised shot noise Cox processes. Advances in Applied Probability, 37, 48-74.
• J. Møller (2005). Properties of spatial Cox process models. Journal of Statistical Research of Iran, 2, 1-18.
• G. Döge, K. Mecke, J. Møller, D. Stoyan and R.P. Waagepetersen (2004). Grand canonical simulations of hard-disk systems by simulated tempering. International Journal of Modern Physics C, 15, 129-147.
• J. Møller (2003). Shot noise Cox processes. Advances in Applied Probability, 35, 614-640.
• P.G. Blackwell and J. Møller (2003). Bayesian analysis of deformed tessellation models. Advances in Applied Probability, 35, 4-26.
• K.K. Berthelsen and J. Møller (2003). 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.
• K.K. Berthelsen and J. Møller (2002). A primer on perfect simulation for spatial point processes. Bulletin of the Brazilian Mathematical Society, 33, 351-367.
• M.B. Hansen, J. Møller and F.Aa. Tøgersen (2002). 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 (2002). Corrigendum: perfect slice samplers. Journal of Royal Statistical Society: Series B (Statistical Methodology), 64, 581.
• O.F. Christensen, J. Møller and R.P. Waagepetersen (2001). Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models. Methodology and Computing in Applied Probability, 3, 309-327.
• J. Møller and Ø. Skare (2001). Coloured Voronoi tessellations for Bayesian image analysis and reservoir modelling. Statistical Modelling, 1, 213-232.
• S. Mase, J. Møller, D. Stoyan, R.P. Waagepetersen and G. Döge (2001). Packing densities and simulated tempering for hard core Gibbs point processes. Annals of the Institute of Statistical Mathematics, 53, 661-680.
• A. Brix and J. Møller (2001). Space-time multi type log Gaussian Cox processes with a view to modelling weed data. Scandinavian Journal of Statistics, 28, 471-488.
• A. Mira, J. Møller and G.O. Roberts (2001). Perfect slice samplers. Journal of Royal Statistical Society: Series B (Statistical Methodology), 63, 593-606.
• W.S. Kendall and J. Møller (2000). Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes. Advances in Applied Probability, 32:844-865.
• A. Baddeley, J. Møller and R. Waagepetersen (2000). Non- and semi-parametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica, 54:329-350.
• J. Møller and K. Schladitz (1999). Extensions of Fill's algorithm for perfect simulation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61:955-969.
• J. Møller (1999). Perfect simulation of conditionally specified models. Journal of Royal Statistical Society: Series B (Statistical Methodology), 61:251-264.
• O. Häggström, M.N.M. van Lieshout and J. Møller (1999). Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes. Bernoulli, 5:641-659.
• J. Møller, A.R. Syversveen and R.P. Waagepetersen (1998). Log Gaussian Cox processes. Scandinavian Journal of Statistics, 25:451-482.
• J. Møller and R.P. Waagepetersen (1998). Markov connected component fields. Advances in Applied Probability (SGSA), 30:1-35.
• A.J. Baddeley, M.N.M. van Lieshout and J. Møller (1996). Markov properties of cluster processes. Advances in Applied Probability (SGSA), 28:346-355.
• J. Møller and S. Zuyev (1996). Gamma-type results and other related properties of Poisson processes. Advances in Applied Probability (SGSA), 28:662-673.
• H. Högmander and J. Møller (1995). Estimating distribution maps from atlas data using methods of statistical image analysis. Biometrics, 51:393-404.
• J. Møller (1995). Generation of Johnson-Mehl crystals and comparative analysis of models for random nucleation. Advances in Applied Probability (SGSA), 27:367-383.
• C.J. Geyer and J. Møller (1994). Simulation procedures and likelihood inference for spatial point processes. Scandinavian Journal of Statistics, 21:359-373.
• J. Møller and M. Sørensen (1994). Statistical analysis of a spatial birth-and-death process model with a view to modeling linear dune fields. Scandinavian Journal of Statistics, 21:1-19.
• J. Møller (1992). Random Johnson-Mehl tessellations. Advances in Applied Probability, 24:814-844.
• J.L. Jensen and J. Møller (1991). Pseudolikelihood for exponential family models of spatial point processes. Annals of Applied Probability, 3:445-461.
• A. Baddeley and J. Møller (1989). Nearest-neighbour Markov point processes and random sets. International Statistical Review, 2:89-121.
• J. Møller (1989). On the rate of convergence of spatial birth-and-death processes. Annals of the Institute of Statistical Mathematics, 3:565-581.
• J. Møller (1989). Random tessellations in R^d. Advances in Applied Probability, 21:37-73.
• J. Møller (1988). Stereological analysis of particles of varying ellipsoidal shape. Journal of Applied Probability, 25:322-335.
• E.B. Jensen and J. Møller (1986). Stereological versions of integral geometric formulae for n-dimensional ellipsoids. Journal of Applied Probability, 23:1031-1037.
• J. Møller (1986). Bartlett adjustments for structured covariances. Scandinavian Journal of Statistics, 13:1-15.

Peer-reviewed book chapters

• J. Møller and D. Stoyan (2016). 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.
• J. Møller (2010). Parametric methods. Chapter 19 in A Handbook of Spatial Statistics, edited by A.E. Gelfand, P. Diggle, M. Fuentes, and P. Guttorp, Chapman and Hall/CRC Press, 317-337.
• J. Møller (2010). 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 K. Mengersen (2007). Ergodic averages 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.
• K.K. Berthelsen and J. Møller (2006). 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.
• J. Møller (2003). 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 and R.P. Waagepetersen (2003). An introduction to simulation based inference for spatial point processes. In Spatial Statistics and Computational Methods, Ed. J. Møller, Lecture Notes in Statistics, 173, Springer-Verlag, 143-198.
• K.K. Berthelsen and J. Møller (2002). Spatial jump processes and perfect simulation. In Morphology of Condensed Matter, Eds. K. Mecke and D. Stoyan, Lecture Notes in Physics, Vol. 600, Springer-Verlag, 391-417.
• J. Møller and R.P. Waagepetersen (2002). Statistical inference for Cox processes. Spatial Cluster Modelling, Eds. Andrew B. Lawson and David Denison, Chapman and Hall/CRC, 37-60.
• J. Møller (2001). 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.
• J. Møller (1999). 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 (1999). 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 (1998). A review on probabilistic models and results for Voronoi tessellations. In Voronoi's Impact on Modern Science, Eds. P. Engel and H. Syta, Institute of Mathematics of the National Academy of Sciences of Ukraine, Kyiv, 254-265.

Peer-reviewed conference proceedings

• J. Møller and K. Helisova (2009). 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 (2009). 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 (2009). 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 J.G. Rasmussen (2009). 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. Møller and J.G. Rasmussen (2004). 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 (2004). 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.
• V. Benes, K. Bodlak, J. Møller and R. Waagepetersen (2003). Application of log Gaussian Cox processes in disease mapping. Proceedings of the ISI Conference on Environmental Statistics and Health, Santiago de Compostela. Eds. J. Mateu, D. Holland and W. Gonzalez-Manetiga, 95-105.
• A. Mira, J. Møller and G. O. Roberts (2001). Perfect simple slice samplers. In Bulletin of the International Statistical Institute, 53rd Session Proceedings, Tome LIX, Book 1. 73-79.
• J. Møller, A.R. Syversveen and R. Waagepetersen (1997). Log Gaussian Cox processes: A statistical model for analyzing stand structural heterogeneity in forestry. In Proc. First European Conference for Information Technology in Agriculture, Eds. H. Kure, I. Thysen and A.R. Kristensen, Department of Mathematics and Physics, The Royal Veterinary and Agricultural University, Denmark.

Discussion contributions

• C.A.N. Biscio and J. Møller (2021). Discussion of `Event history and topological data analysis´. Biometrika, 108, 779-783.
• J. Møller (2012). 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 (2011). Contribution to the discussion of Lindgren, F., Rue, H. and Lindstrøm, 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.
• J. Møller (2008). Contribution to the discussion of P. McCullagh (2008): Sampling bias and logistic models. Journal of Royal Statistical Society: Series B (Statistical Methodology), 70, 669.
• J. Møller (2006). 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.
• J. Møller (2003). Contribution to the discussion of S.P. Brooks, P. Giudici and G.O. Roberts (2003): Efficient constructions of reversible jump Markov chain Monte Carlo proposal distributions. Journal of Royal Statistical Society: Series B (Statistical Methodology), 65, 42-43.
• J. Møller and R.P. Waagepetersen (1999). Contribution to the discussion of J. Besag and D. Higdon (1999): Bayesian analysis of agricultural field experiments. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61, 735.
• J. Møller (1994). Contribution to the discussion of N.L. Hjort and H. Omre (1994): Topics in spatial statistics. Scandinavian Journal of Statistics, 21:346-349.
• J. Møller (1993). Contribution to the discussion on the meeting on the Gibbs Sampler and other Markov Chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 55:84-85.
• J. Møller (1992). Contribution to the discussion of C.J. Geyer and E.A. Thompson (1992): Constrained Monte Carlo maximum likelihood for dependent data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 54:692-693.

Memorial

• J. Møller (2011). Memorial: Julian Besag 1945-2010. See also the booklet (save it as a link).

Book reviews

• J. Møller (2009). "Statistical Analysis and Modelling of Spatial Point Patterns" by J. Illian, A. Penttinen, H. Stoyan and D. Stoyan. Biometrika, 65: 995.
• J. Møller (1992). "Stochastische Geometrie" by J. Meche, R. Scheider, D. Stoyan and W. Weil. Sonderdruck aus Jahresbericht der Deutchen Mathematiker Vereinigung, Bd 94, Heft 3, 2. Abteilung, 49-50.

Technical reports

• I.W. Herbst, J. Møller and A.M. Svane (2024). The asymptotic distribution of the scaled remainder for pseudo golden ratio expansions of a continuous random variable. Available at arXiv:2404.08387.
• 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 (2016). 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.
• P.G.M. Forbes, S. Lauritzen and J. Møller (2014). Fingerprint analysis with marked point processes. Research Report R-2014-06, Department of Mathematical Sciences, Aalborg University. Submitted for journal publication.
• F. Lavancier, J. Møller and E. Rubak (2014). Determinantal point process models and statistical inference: Extended version. Available at arXiv:1205.4818.
• E. Rubak, J. Møller and P. McCullagh (2010). Statistical inference for a class of multivariate negative binomial distributions. Research Report R-2010-10, Department of Mathematical Sciences, Aalborg University.
• J. Møller and M.L. Yiu (2009). The distribution of communication cost for a mobile service scenario. Research Report R-2009-11, Department of Mathematical Sciences, Aalborg University.
• J. Møller and G. Nicholls (1999). Perfect simulation for sample-based inference. Research Report R-99-2011, Department of Mathematical Sciences, Aalborg University. Conditionally accepted for Statistics and Computing.
• W.S. Kendall and J. Møller (1999). 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 (1999). Perfect implementation of a Metropolis-Hastings simulation of Markov point processes. Research Report 348, Department of Statistics, University of Warwick.
• J. Møller (1992). Extensions of the Swendsen-Wang algorithm for simulating spatial point processes. Research Report 246, Department of Theoretical Statistics, University of Aarhus.
• J. Møller, E.B. Jensen, J.S. Petersen and H.J.G. Gundersen (1989). Modelling an aggregate of space-filling cells from sectional data. Research Report 182, Department of Theoretical Statistics, University of Aarhus.
• J. Møller (1985). A simple derivation of a formula of Blaschke and Petkantschin. Research Report 138, Department of Theoretical Statistics, University of Aarhus.
• J. Møller (1985). On the accuracy of the $\chi$-squared approximation of the Bartlett adjusted log likelihood ratio statistic for three testing problems. Research Report 129, Department of Theoretical Statistics, University of Aarhus.

Lecture notes

• K.K. Berthelsen and J. Møller (2004). 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.
• J. Møller og R.P. Waagepetersen (2003). Lidt om kurver og geometrisk kontinuitet. Institut for Matematiske Fag, Aalborg Universitet.
• J. Møller (1999). Notes on Markov chain Monte Carlo methods. Department of Mathematical Sciences, Aalborg University.
• J. Møller (1995). Centrale Statistiske Modeller og Likelihood Baserede Metoder. Institute of Mathematical Sciences, University of Aarhus. (285 pages.)
• J. Møller (1990). Lectures on Markov random fields. Department of Theoretical Statistics, University of Aarhus.
• J. Møller (1989). Stokastiske Tessellationer. Department of Theoretical Statistics, University of Aarhus.