Professor Dirk Kroese
Professor

Dirk Kroese

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+61 7 336 53287

Background

Dirk Kroese's research interests are in: Monte Carlo methods, rare-event simulation, the cross-entropy method, applied probability, and randomised optimisation.

Dirk Kroese is a professor of Mathematics and Statistics at the School of Mathematics and Physics of the University of Queensland. He has held teaching and research positions at The University of Texas at Austin, Princeton University, the University of Twente, the University of Melbourne, and the University of Adelaide. His research interests include Monte Carlo methods, adaptive importance sampling, randomized optimization, and rare-event simulation. He has over 120 peer-reviewed publications, including six monographs:

  • Rubinstein, R.Y., Kroese, D.P. (2004). The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning, Springer, New York.
  • Rubinstein, R. Y. , Kroese, D. P. (2007). Simulation and the Monte Carlo Method, 2nd edition, John Wiley & Sons.
  • Kroese, D.P., Taimre, T., and Botev, Z.I. (2011). Handbook of Monte Carlo Methods, Wiley Series in Probability and Statistics, John Wiley & Sons, New York.
  • Kroese, D.P. and Chan, J.C.C. (2014). Statistical Modeling and Computation, Springer, New York.
  • Rubinstein, R. Y. , Kroese, D. P. (2017). Simulation and the Monte Carlo Method, 3rd edition, John Wiley & Sons.
  • Kroese, D.P., Botev, Z.I., Taimre, T and Vaisman, R. (2019) Data Science and Machine Learning: Mathematical and Statistical Methods, Chapman & Hill/CRC.
  • Kroese, D.P. and Botev, Z.I. (2023). An Advanced Course in Probability and Stochastic Processes, Chapman & Hill/CRC.

Availability

Professor Dirk Kroese is:
Not available for supervision
Media expert

Fields of research

Applied mathematics Numerical and computational mathematics Statistics

Qualifications

  • Bachelor of Science, University of Twente
  • Masters (Coursework) of Science, University of Twente
  • Doctor of Philosophy, University of Twente

Research interests

  • The Cross-Entropy Method

    The CE methods involves an iterative procedure where each iteration can be broken down into two phases: (a) generate a randon data sample (trajectories, vectors, etc.) according to a specific mechanism; (b) update the parameters of the randdom mechanism based on this data in order to produce a better sample in the next iteration. I am one of the pioneers of the CE method. The simplicity and versatility of the method is explained in my book with R.Y. Rubinstein: The Cross Entropy Method: A Unified Approach to Combinatorial Optimisation. Monte-Carlo Simulation, and Machine Learning, Springer Verlag, 2004. The CE method has been applied to problems in systems reliability, buffer allocation, telecommunication systems, neural computation, control and navigation, DNA sequence alignment, scheduling and many more.

  • Monte Carlo Methods

    To better understand randomness, it is useful to perform random experiments on a computer. Such "Monte Carlo simulations" are nowadays an essential ingredient in many scientific investigations. Monte Carlo can be used in several different ways: (1) to mimic a random process so as to observe its behaviour, (2) to estimate numerical quantities (e.g., multidimensional integrals) via repeated simulation, and (3) to optimise a complicated (e.g., highly multi-modal) function.

Search Professor Dirk Kroese’s works on UQ eSpace

138 works between 1989 and 2024
All (138) Journal Article (81) Book Chapter (8) Conference Publication (37) Book (9) Other Outputs (2) Edited Outputs (1)

81 - 100 of 138 works

2007

Conference Publication

Identifying change-points in biological sequences via sequential importance sampling

Sofronov, G. Y., Evans, G. E., Keith, J. M. and Kroese, D. P. (2007). Identifying change-points in biological sequences via sequential importance sampling. 17th Biennial Congress on Modelling and Simulation (MODSIM07), Christchurch, New Zealand, 10-13 December, 2007. Christchurch, New Zealand: Modelling and Simulation Society of Australia and New Zealand.

Identifying change-points in biological sequences via sequential importance sampling

2007

Book

Simulation and the Monte Carlo Method: Solutions Manual to Accompany

Kroese, Dirk P., Taimre, Thomas, Botev, Zdravko I. and Rubinstein, Rueven Y. (2007). Simulation and the Monte Carlo Method: Solutions Manual to Accompany. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470285312

Simulation and the Monte Carlo Method: Solutions Manual to Accompany

2007

Journal Article

Stochastic models for the spread of HIV in a mobile heterosexual population

Sani, A., Kroese, D. P. and Pollett, P. K. (2007). Stochastic models for the spread of HIV in a mobile heterosexual population. Mathematical Biosciences, 208 (1), 98-124. doi: 10.1016/j.mbs.2006.09.024

Stochastic models for the spread of HIV in a mobile heterosexual population

2007

Journal Article

Generalized cross-entropy methods with applications to rare-event simulation and optimization

Botev, Z. I., Kroese, D. P. and Taimre, T. (2007). Generalized cross-entropy methods with applications to rare-event simulation and optimization. Simulation, 83 (11), 785-806. doi: 10.1177/0037549707087067

Generalized cross-entropy methods with applications to rare-event simulation and optimization

2007

Conference Publication

Parallel Cross-Entropy optimization

Evans, Gareth E., Keith, Jonathan M. and Kroese, Dirk P. (2007). Parallel Cross-Entropy optimization. 2007 Winter Simulation Conference, Washington Dc, Dec 09-12, 2007. NEW YORK: IEEE.

Parallel Cross-Entropy optimization

2007

Journal Article

Estimating the number of s-t paths in a graph

Roberts, B. and Kroese, D. P. (2007). Estimating the number of s-t paths in a graph. Journal of Graph Algorithms and Applications, 11 (1), 195-214. doi: 10.7155/jgaa.00142

Estimating the number of s-t paths in a graph

2007

Conference Publication

Optimal epidemic intervention of HIV spread using the cross-entropy method

Sani, A. and Kroese, D. P. (2007). Optimal epidemic intervention of HIV spread using the cross-entropy method. 17th Biennial Congress on Modelling and Simulation (MODSIM07), Christchurch, New Zealand, 10-13 December, 2007. Christchurch, New Zealand: Modelling and Simulation Society of Australia and New Zealand.

Optimal epidemic intervention of HIV spread using the cross-entropy method

2007

Book Chapter

Applications of the cross-entropy method in reliability

Kroese, D. P. and Hui, Kin-Ping (2007). Applications of the cross-entropy method in reliability. Computational intelligence in reliability engineering. New metaheuristics, neural and fuzzy techniques in reliability. (pp. 37-82) edited by Gregory Levitin. Berlin, Germany: Springer-Verlag. doi: 10.1007/978-3-540-37372-8_3

Applications of the cross-entropy method in reliability

2007

Journal Article

Application of the cross-entropy method to clustering and vector quantization

Kroese, Dirk P., Rubinstein, Reuven Y. and Taimre, Thomas (2007). Application of the cross-entropy method to clustering and vector quantization. Journal of Global Optimization, 37 (1), 137-157. doi: 10.1007/s10898-006-9041-0

Application of the cross-entropy method to clustering and vector quantization

2007

Book

Solutions manual to accompany simulation and the Monte Carlo Method

Kroese, Dirk P., Taimre, Thomas, Botev, Zdravko I. and Rubinstein, Reuven Y. (2007). Solutions manual to accompany simulation and the Monte Carlo Method. 2nd ed. Hoboken, N.J., U.S.A.: Wiley-Interscience.

Solutions manual to accompany simulation and the Monte Carlo Method

2007

Journal Article

Network reliability optimization via the cross-entropy method

Kroese, D. P., Hui, K. P. and Nariai, S. (2007). Network reliability optimization via the cross-entropy method. IEEE Transactions on Reliability, 56 (2), 275-287. doi: 10.1109/TR.2007.895303

Network reliability optimization via the cross-entropy method

2007

Conference Publication

Parallel cross-entropy optimization

Evans, G. E., Keith, J. M. and Kroese, D. P. (2007). Parallel cross-entropy optimization. 2007 Winter Simulation Conference, Washington, 9-12 December, 2007. Washington: IEEE. doi: 10.1145/1360000/1351930/p2196-evans.pdf?key1=1351930

Parallel cross-entropy optimization

2007

Journal Article

Convergence properties of the cross-entropy method for discrete optimization

Costa, A., Jones, O. D. and Kroese, D. P. (2007). Convergence properties of the cross-entropy method for discrete optimization. Operations Research Letters, 35 (5), 573-580. doi: 10.1016/j.orl.2006.11.005

Convergence properties of the cross-entropy method for discrete optimization

2006

Conference Publication

Generalized cross-entropy methods for rare events and optimization

Botev, Z. I., Kroese, D. P. and Taimre, T. (2006). Generalized cross-entropy methods for rare events and optimization. 6th International Workshop on Rare Event Simulation (RESIM 2006), Bamberg, Germany, 8-10 October, 2006.

Generalized cross-entropy methods for rare events and optimization

2006

Journal Article

Improved algorithms for rare event simulation with heavy tails

Asmussen, Søren and Kroese, Dirk P. (2006). Improved algorithms for rare event simulation with heavy tails. Advances In Applied Probability, 38 (2), 545-558. doi: 10.1239/aap/1151337084

Improved algorithms for rare event simulation with heavy tails

2006

Journal Article

The cross-entropy method for continuous multi-extremal optimization

Kroese, Dirk P., Porotsky, Sergey and Rubinstein, Reuven Y. (2006). The cross-entropy method for continuous multi-extremal optimization. Methodology and Computing In Applied Probability, 8 (3), 383-407. doi: 10.1007/s11009-006-9753-0

The cross-entropy method for continuous multi-extremal optimization

2006

Journal Article

An optimal sequential procedure for a buying-selling problem with independent observations

Sofronov, G, Keith, JM and Kroese, DP (2006). An optimal sequential procedure for a buying-selling problem with independent observations. Journal of Applied Probability, 43 (2), 454-462. doi: 10.1239/jap/1152413734

An optimal sequential procedure for a buying-selling problem with independent observations

2005

Conference Publication

On the Design of Multi-type Networks via the Cross-Entropy Method

Nariai, S. and Kroese, D. P. (2005). On the Design of Multi-type Networks via the Cross-Entropy Method. DRCN 2005, Naples, Italy, 16-19 October 2005. Italy: IEEE. doi: 10.1109/DRCN.2005.1563852

On the Design of Multi-type Networks via the Cross-Entropy Method

2005

Journal Article

A tutorial on the cross-entropy method

De Boer, Pieter-Tjerk, Kroese, Dirk P., Mannor, Shie and Rubinstein, Reuven Y. (2005). A tutorial on the cross-entropy method. Annals of Operations Research, 134 (1), 19-67. doi: 10.1007/s10479-005-5724-z

A tutorial on the cross-entropy method

2005

Journal Article

Heavy tails, importance sampling and cross-entropy

Asmussen, S., Kroese, D. P. and Rubinstein, R. Y. (2005). Heavy tails, importance sampling and cross-entropy. Stochastic Models, 21 (1), 57-76. doi: 10.1081/STM-200046472

Heavy tails, importance sampling and cross-entropy

Past funding

  • 2021 - 2024
    Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries
    ARC Discovery Projects
    Open grant
  • 2018 - 2022
    High Quality and Robust Energy Conversion Systems for Distribution Networks
    ARC Linkage Projects
    Open grant
  • 2017 - 2019
    Large Scale Sequential Decision Making in an Uncertain World.
    United States Office of Naval Research
    Open grant
  • 2014 - 2021
    ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights (University of Melbourne lead institution)
    University of Melbourne
    Open grant
  • 2014 - 2016
    Advanced Monte Carlo Methods for Spatial Processes
    ARC Discovery Projects
    Open grant
  • 2012 - 2013
    Monte Carlo Methods for Spatial Stochastic Modeling
    Go8 Australia - Germany Joint Research Co-operation Scheme
    Open grant
  • 2011
    New-generation parallel-computing cluster for the mathematical and physical sciences
    UQ Major Equipment and Infrastructure
    Open grant
  • 2009 - 2013
    Improved Monte Carlo Methods for Estimation, Optimisation, and Counting
    ARC Discovery Projects
    Open grant
  • 2005 - 2007
    Cross-Entropy Methods in Complex Biological Systems
    ARC Discovery Projects
    Open grant
  • 2005 - 2007
    Rare Event Simulation with Heavy Tails
    ARC Discovery Projects
    Open grant
  • 2002
    Financial markets and network bandwidth
    University of Queensland Research Development Grants Scheme
    Open grant
  • 2000 - 2001
    Rare event simulation
    UQ New Staff Research Start-Up Fund
    Open grant

Availability

Professor Dirk Kroese is:
Not available for supervision

Supervision history

Current supervision

  • Doctor Philosophy

    L\'{e}vy Processes: Theory and Applications

    Associate Advisor

    Other advisors: Dr Kazutoshi Yamazaki

  • Master Philosophy

    Improved Exploration Methods for Deep Reinforcement Learning

    Associate Advisor

    Other advisors: Dr Nan Ye

  • Doctor Philosophy

    Reinforcement Learning for Partially Observable Environments

    Associate Advisor

    Other advisors: Dr Nan Ye

  • Doctor Philosophy

    Reinforcement Learning for Large and Complex Partially Observable Markov Decision Processes

    Associate Advisor

    Other advisors: Dr Nan Ye

  • Doctor Philosophy

    Statistical Models of Extreme Weather Events in a Changing Climate

    Associate Advisor

    Other advisors: Dr Meagan Carney

Completed supervision

Enquiries

Contact Professor Dirk Kroese directly for media enquiries about:

  • Monte Carlo simulation

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