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)

61 - 80 of 138 works

2011

Conference Publication

Fitting mixture importance sampling distributions via improved cross-entropy

Brereton, Tim J., Chan, Joshua C. C. and Kroese, Dirk P. (2011). Fitting mixture importance sampling distributions via improved cross-entropy. 2011 Winter Simulation Conference, Phoenix, AZ, United States, 11-14 December 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/WSC.2011.6147769

Fitting mixture importance sampling distributions via improved cross-entropy

2011

Journal Article

Estimating change-points in biological sequences via the cross-entropy method

Evans, G. E., Sofronov, G. Y., Keith, J. M. and Kroese, D. P. (2011). Estimating change-points in biological sequences via the cross-entropy method. Annals of Operations Research, 189 (1), 155-165. doi: 10.1007/s10479-010-0687-0

Estimating change-points in biological sequences via the cross-entropy method

2011

Journal Article

A comparison of cross-entropy and variance minimization strategies

Chan, Joshua. C., Glynn, Peter W. and Kroese, Dirk P. (2011). A comparison of cross-entropy and variance minimization strategies. Journal of Applied Probability, 48 A (A), 1-15. doi: 10.1239/jap/1318940464

A comparison of cross-entropy and variance minimization strategies

2011

Conference Publication

Greedy servers on a torus

Stacey, Karl W. and Kroese, Dirk P. (2011). Greedy servers on a torus. 2011 Winter Simulation Conference, Phoenix, AZ, United States, 11-14 December 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/WSC.2011.6147764

Greedy servers on a torus

2011

Book

Handbook of Monte Carlo Methods

Kroese, Dirk P., Taimre, Thomas and Botev, Zdravko I. (2011). Handbook of Monte Carlo Methods. Hoboken, NJ, U.S.A.: John Wiley & Sons. doi: 10.1002/9781118014967

Handbook of Monte Carlo Methods

2010

Journal Article

Kernel density estimation via diffusion

Botev, Z. I., Grotowski, J. F. and Kroese, D. P. (2010). Kernel density estimation via diffusion. Annals of Statistics, 38 (5), 2916-2957. doi: 10.1214/10-AOS799

Kernel density estimation via diffusion

2010

Journal Article

Efficient estimation of large portfolio loss probabilities in t-copula models

Chan, Joshua C. C. and Kroese, Dirk P. (2010). Efficient estimation of large portfolio loss probabilities in t-copula models. European Journal of Operational Research, 205 (2), 361-367. doi: 10.1016/j.ejor.2010.01.003

Efficient estimation of large portfolio loss probabilities in t-copula models

2010

Other Outputs

Improved Cross-Entropy Method for Estimation

Joshua C. C. Chan and Dirk P. Kroese (2010). Improved Cross-Entropy Method for Estimation. School of Economics, University of Queensland.

Improved Cross-Entropy Method for Estimation

2010

Book Chapter

Cross-entropy method

Kroese, Dirk P. (2010). Cross-entropy method. Encyclopedia of operations research and management sciences. (pp. 1-12) New York, United States: Springer-Verlag. doi: 10.1002/9780470400531.eorms0210

Cross-entropy method

2009

Journal Article

Identifying Change-Points in Biological Sequences via Sequential Importance Sampling

Sofronov, George Yu., Evans, Gareth E., Keith, Jonathan M. and Kroese, Dirk P (2009). Identifying Change-Points in Biological Sequences via Sequential Importance Sampling. Environmental Modeling & Assessment, 14 (5), 577-584. doi: 10.1007/s10666-008-9160-8

Identifying Change-Points in Biological Sequences via Sequential Importance Sampling

2009

Conference Publication

Optimal generation expansion planning via the cross-entropy method

Kothari, Rishabh P. and Kroese, Dirk P. (2009). Optimal generation expansion planning via the cross-entropy method. 2009 Winter Simulation Conference (ERA Rank B), Austin, Texas, 13-16 December 2009. United States: IEEE - Inst Electrical Electronics Engineers Inc. doi: 10.1109/WSC.2009.5429296

Optimal generation expansion planning via the cross-entropy method

2008

Journal Article

Non-asymptotic bandwidth selection for density estimation of discrete data

Botev, Z. I. and Kroese, D. P. (2008). Non-asymptotic bandwidth selection for density estimation of discrete data. Methodology And Computing In Applied Probability, 10 (3), 435-451. doi: 10.1007/s11009-007-9057-z

Non-asymptotic bandwidth selection for density estimation of discrete data

2008

Journal Article

Adaptive independence samplers

Keith, J. M., Kroese, D. P. and Sofronov, G. Y. (2008). Adaptive independence samplers. Statistics and Computing, 18 (4), 409-420. doi: 10.1007/s11222-008-9070-2

Adaptive independence samplers

2008

Journal Article

Truck fleet model for design and assessment of flexible pavements

Belay, A., O'Brien, E. and Kroese, D. P. (2008). Truck fleet model for design and assessment of flexible pavements. Journal of Sound and Vibration, 311 (3-5), 1161-1174. doi: 10.1016/j.jsv.2007.10.019

Truck fleet model for design and assessment of flexible pavements

2008

Journal Article

An efficient algorithm for rare-event probability estimation, combinatorial optimization, and counting

Botev, Z. I. and Kroese, D. P. (2008). An efficient algorithm for rare-event probability estimation, combinatorial optimization, and counting. Methodology And Computing In Applied Probability, 10 (4), 471-505. doi: 10.1007/s11009-008-9073-7

An efficient algorithm for rare-event probability estimation, combinatorial optimization, and counting

2008

Conference Publication

The Generalized Gibbs Sampler and the Neighborhood Sampler

Keith, J. M., Sofronov, G. Y. and Kroese, D. P. (2008). The Generalized Gibbs Sampler and the Neighborhood Sampler. 7th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, Ulm, Germany, 14-18 August, 2006. Berlin: Springer-Verlag. doi: 10.1007/978-3-540-74496-2_31

The Generalized Gibbs Sampler and the Neighborhood Sampler

2008

Conference Publication

Randomized methods for solving the Winner Determination Problem in combinatorial auctions

Chan, J. C. C. and Kroese, D. P. (2008). Randomized methods for solving the Winner Determination Problem in combinatorial auctions. Winter Simulation Conference 2008 (WSC 2008), Miami, United States, 7-10 December, 2008. Piscataway, NJ, U.S.A.: IEEE. doi: 10.1109/WSC.2008.4736208

Randomized methods for solving the Winner Determination Problem in combinatorial auctions

2008

Journal Article

Controlling the number of HIV infectives in a mobile population

Sani, A. and Kroese, D. P. (2008). Controlling the number of HIV infectives in a mobile population. Mathematical Biosciences, 213 (2), 103-112. doi: 10.1016/j.mbs.2008.03.003

Controlling the number of HIV infectives in a mobile population

2008

Book

Simulation and the Monte Carlo Method

Rubinstein, Reuven Y. and Kroese, Dirk P. (2008). Simulation and the Monte Carlo Method. 2nd ed. New York, United States: John Wiley & Sons. doi: 10.1002/9780470230381

Simulation and the Monte Carlo Method

2007

Journal Article

Applications of the cross-entropy method in reliability

Kroese, Dirk P. and Hui, Kin-Ping (2007). Applications of the cross-entropy method in reliability. Studies in Computational Intelligence, 40, 37-82. doi: 10.1007/978-3-540-37372-8_3

Applications of the cross-entropy method in reliability

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

    Reinforcement Learning for Partially Observable Environments

    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

  • Master Philosophy

    Improved Exploration Methods for Deep Reinforcement Learning

    Associate Advisor

    Other advisors: Dr Nan Ye

  • Doctor Philosophy

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

    Associate Advisor

    Other advisors: Dr Kazutoshi Yamazaki

  • Doctor Philosophy

    Reinforcement Learning for Large and Complex Partially Observable Markov Decision Processes

    Associate Advisor

    Other advisors: Dr Nan Ye

Completed supervision

Enquiries

Contact Professor Dirk Kroese directly for media enquiries about:

  • Monte Carlo simulation

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