What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Normal distribution
His primary areas of study are Monte Carlo method, Mathematical optimization, Cross-entropy method, Importance sampling and Markov chain Monte Carlo.
His Monte Carlo method study combines topics from a wide range of disciplines, such as Probability and statistics, Statistical physics, Artificial intelligence and Operations research.
His Cross-entropy method research includes themes of Stochastic optimization, Cross entropy, Theory of computation and Theoretical computer science.
Dirk P. Kroese interconnects Algorithm, Bounded function, Independent and identically distributed random variables and Control variates in the investigation of issues within Importance sampling.
Dirk P. Kroese has researched Algorithm in several fields, including Smoothing, Sample, Density estimation and Kernel density estimation.
His Slice sampling, Rejection sampling and Monte Carlo integration study in the realm of Markov chain Monte Carlo interacts with subjects such as Contemporary science.
His most cited work include:
- Simulation and the Monte Carlo Method (1517 citations)
- A Tutorial on the Cross-Entropy Method (1243 citations)
- Kernel density estimation via diffusion (1109 citations)
What are the main themes of his work throughout his whole career to date?
Dirk P. Kroese mostly deals with Mathematical optimization, Monte Carlo method, Algorithm, Cross-entropy method and Importance sampling.
His work focuses on many connections between Mathematical optimization and other disciplines, such as Cross entropy, that overlap with his field of interest in Rare events.
His research on Monte Carlo method frequently connects to adjacent areas such as Statistical physics.
The concepts of his Statistical physics study are interwoven with issues in Statistics and Random field.
His Algorithm research incorporates themes from Theoretical computer science and Markov chain.
His Cross-entropy method study is associated with Combinatorial optimization.
He most often published in these fields:
- Mathematical optimization (40.39%)
- Monte Carlo method (24.14%)
- Algorithm (18.72%)
What were the highlights of his more recent work (between 2014-2021)?
- Mathematical optimization (40.39%)
- Monte Carlo method (24.14%)
- Algorithm (18.72%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Mathematical optimization, Monte Carlo method, Algorithm, Applied mathematics and Sampling.
His research in Mathematical optimization intersects with topics in Partially observable Markov decision process, Markov decision process and Cross entropy.
His work deals with themes such as Estimator and Reliability, which intersect with Monte Carlo method.
The Algorithm study combines topics in areas such as Tessellation, Markov chain Monte Carlo, Maxima and minima, Laguerre polynomials and Stochastic optimization.
The various areas that he examines in his Tessellation study include Cross-entropy method, Tomographic image and Inverse problem.
In his study, Mixture model, Rejection sampling and Process simulation is strongly linked to Point process, which falls under the umbrella field of Applied mathematics.
Between 2014 and 2021, his most popular works were:
- Spatial Process Simulation (51 citations)
- Fitting Laguerre tessellation approximations to tomographic image data (19 citations)
- Unbiased and consistent nested sampling via sequential Monte Carlo (16 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Algorithm
Mathematical optimization, Algorithm, Cross entropy, Cross-entropy method and Monte Carlo method are his primary areas of study.
The study incorporates disciplines such as Range, Planner and Kullback–Leibler divergence in addition to Mathematical optimization.
His Algorithm study incorporates themes from Nested sampling algorithm, Sequential monte carlo methods, Markov chain Monte Carlo and Special case.
His studies in Cross entropy integrate themes in fields like Continuous optimization, Discrete optimization, Combinatorial optimization and Scale.
His Cross-entropy method research incorporates elements of Tessellation, Inverse problem, Maxima and minima, Laguerre polynomials and Stochastic optimization.
He has included themes like Network topology, Reliability and Communications system in his Monte Carlo method study.
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