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- Jesper Møller

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
41
Citations
10,817
235
World Ranking
1259
National Ranking
10

- Statistics
- Geometry
- Mathematical analysis

His main research concerns Point process, Statistical physics, Markov chain Monte Carlo, Markov chain and Statistics. His research in Point process intersects with topics in Coupling from the past, Covariate, Applied mathematics, Algorithm and Point. His studies deal with areas such as Estimation theory, Mathematical optimization, Field and Metropolis–Hastings algorithm as well as Statistical physics.

His Markov chain Monte Carlo study incorporates themes from Spatial analysis and Bayesian inference. The Markov chain study combines topics in areas such as Discrete mathematics, Moment measure, Slice sampling and Monte Carlo method. His Cox process and Pseudolikelihood study, which is part of a larger body of work in Statistics, is frequently linked to Q–Q plot, Context and Kernel smoother, bridging the gap between disciplines.

- Statistical Inference and Simulation for Spatial Point Processes (839 citations)
- Log Gaussian Cox Processes (546 citations)
- Non-and semi-parametric estimation of interaction in inhomogeneous point patterns (441 citations)

Jesper Møller spends much of his time researching Point process, Statistical physics, Combinatorics, Pure mathematics and Statistics. His research integrates issues of Poisson distribution, Inference, Bayesian inference, Algorithm and Point in his study of Point process. His work deals with themes such as Statistical inference and Parametric model, which intersect with Algorithm.

His Statistical physics study also includes

- Markov chain Monte Carlo that intertwine with fields like Mathematical optimization,
- Markov chain which intersects with area such as Markov process. Jesper Møller has included themes like Discrete mathematics, Finite group and Euler's formula in his Pure mathematics study. His biological study spans a wide range of topics, including Econometrics and Applied mathematics.

- Point process (38.60%)
- Statistical physics (19.85%)
- Combinatorics (15.81%)

- Point process (38.60%)
- Combinatorics (15.81%)
- Statistical physics (19.85%)

Jesper Møller focuses on Point process, Combinatorics, Statistical physics, Pure mathematics and Cox process. His Point process research includes elements of Algorithm, Point and Cluster analysis. Jesper Møller focuses mostly in the field of Point, narrowing it down to topics relating to Bayesian inference and, in certain cases, Expectation–maximization algorithm.

His Combinatorics study combines topics from a wide range of disciplines, such as Probability distribution and Equivariant map. His Statistical physics research incorporates elements of Markov chain Monte Carlo, Cerebral cortex, State, Markov chain and Coupling. Markov chain Monte Carlo is a subfield of Bayesian probability that Jesper Møller studies.

- 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. (29 citations)
- A tutorial on Palm distributions for spatial point processes (23 citations)
- Determinantal point process models on the sphere (23 citations)

- Statistics
- Geometry
- Mathematical analysis

His primary scientific interests are in Point process, Pure mathematics, Determinantal point process, Cox process and Finite set. His Point process research incorporates themes from Distribution, Bayesian inference, Separable space, Point and Coupling. His Distribution study integrates concerns from other disciplines, such as Iterated function, Superposition principle, State, Statistical physics and Markov chain.

His Point research is multidisciplinary, incorporating elements of Algorithm, Statistical inference and Approximate Bayesian computation. His Cox process research focuses on Distribution and how it connects with Statistics. His Statistics research is multidisciplinary, relying on both Inference and Applied mathematics.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Statistical Inference and Simulation for Spatial Point Processes

Jesper Moller;Rasmus Plenge Waagepetersen.

**(2003)**

1552 Citations

Log Gaussian Cox Processes

Jesper Møller;Anne Randi Syversveen;Rasmus Plenge Waagepetersen.

Scandinavian Journal of Statistics **(1998)**

837 Citations

Non-and semi-parametric estimation of interaction in inhomogeneous point patterns

Adrian Baddeley;J. Moller;R. Waagepetersen.

Statistica Neerlandica **(2000)**

716 Citations

Simulation Procedures and Likelihood Inference for Spatial Point Processes

Charles J. Geyer;Jesper Moller.

Scandinavian Journal of Statistics **(1994)**

566 Citations

An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants

Jesper Moller;Anthony N. Pettitt;Robert W. Reeves;Kasper K. Berthelsen.

Biometrika **(2006)**

459 Citations

Residual analysis for spatial point processes (with discussion)

A. Baddeley;R. Turner;Jesper Møller;M. Hazelton.

Journal of The Royal Statistical Society Series B-statistical Methodology **(2005)**

400 Citations

Lectures on Random Voronoi Tessellations

Jesper Møller.

**(1994)**

398 Citations

Discussion on the paper by Feranhead and Prangle

Jesper Møller.

Journal of The Royal Statistical Society Series B-statistical Methodology **(2012)**

362 Citations

Modern Statistics for Spatial Point Processes

Jesper Møller;Rasmus P. Waagepetersen.

Scandinavian Journal of Statistics **(2007)**

334 Citations

Nearest-Neighbour Markov Point Processes and Random Sets

Adrian Baddeley;Jesper Møller.

International Statistical Review **(1989)**

285 Citations

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