What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Point process (38.60%)
- Statistical physics (19.85%)
- Combinatorics (15.81%)
What were the highlights of his more recent work (between 2016-2021)?
- Point process (38.60%)
- Combinatorics (15.81%)
- Statistical physics (19.85%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
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