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)

41 - 60 of 138 works

2014

Journal Article

A critical exponent for shortest-path scaling in continuum percolation

Brereton, Tim, Hirsch, Christian, Schmidt, Volker and Kroese, Dirk (2014). A critical exponent for shortest-path scaling in continuum percolation. Journal of Physics A: Mathematical and Theoretical, 47 (50) 505003, 1-12. doi: 10.1088/1751-8113/47/50/505003

A critical exponent for shortest-path scaling in continuum percolation

2014

Journal Article

Inverting Laguerre tessellations

Duan, Qibin, Kroese, Dirk P., Brereton, Tim, Spettl, Aaron and Schmidt, Volker (2014). Inverting Laguerre tessellations. The Computer Journal, 57 (9), 1431-1440. doi: 10.1093/comjnl/bxu029

Inverting Laguerre tessellations

2014

Journal Article

Automated state-dependent importance sampling for Markov jump processes via sampling from the zero-variance distribution

Grace, Adam W., Kroese, Dirk P. and Sandmann, Werner (2014). Automated state-dependent importance sampling for Markov jump processes via sampling from the zero-variance distribution. Journal of Applied Probability, 51 (3), 741-755. doi: 10.1239/jap/1409932671

Automated state-dependent importance sampling for Markov jump processes via sampling from the zero-variance distribution

2014

Journal Article

Why the Monte Carlo method is so important today

Kroese, Dirk P., Brereton, Tim, Taimre, Thomas and Botev, Zdravko I. (2014). Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics, 6 (6), 386-392. doi: 10.1002/wics.1314

Why the Monte Carlo method is so important today

2014

Journal Article

Efficient simulation of Markov chains using segmentation

Brereton, Tim, Stenzel, Ole, Baumeier, Bjorn, Andrienko, Denis, Schmidt, Volker and Kroese, Dirk (2014). Efficient simulation of Markov chains using segmentation. Methodology and Computing in Applied Probability, 16 (2), 465-484. doi: 10.1007/s11009-013-9327-x

Efficient simulation of Markov chains using segmentation

2014

Book

Statistical Modeling and Computation

Kroese, Dirk P. and Chan, Joshua C. C. (2014). Statistical Modeling and Computation. New York, NY, United States: Springer New York. doi: 10.1007/978-1-4614-8775-3

Statistical Modeling and Computation

2014

Journal Article

A general framework for consistent estimation of charge transport properties via random walks in random environments

Stenzel, Ole, Hirsch, Christian, Brereton, Tim, Baumeier, Bjoern, Andrienko, Denis, Kroese, Dirk and Schmidt, Volker (2014). A general framework for consistent estimation of charge transport properties via random walks in random environments. Multiscale Modeling and Simulation, 12 (3), 1108-1134. doi: 10.1137/130942504

A general framework for consistent estimation of charge transport properties via random walks in random environments

2013

Journal Article

Graph-based simulated annealing: a hybrid approach to stochastic modeling of complex microstructures

Stenzel, O., Westhoff, D., Manke, I., Kasper, M., Kroese, D. P. and Schmidt, V. (2013). Graph-based simulated annealing: a hybrid approach to stochastic modeling of complex microstructures. Modelling and Simulation in Materials Science and Engineering, 21 (5) 055004, 055004.1-055004.18. doi: 10.1088/0965-0393/21/5/055004

Graph-based simulated annealing: a hybrid approach to stochastic modeling of complex microstructures

2013

Book Chapter

The cross-entropy method for estimation

Kroese, Dirk P., Rubinstein, Reuven Y. and Glynn, Peter W. (2013). The cross-entropy method for estimation. Machine learning: theory and applications. (pp. 19-34) edited by Venu Govindaraju and C. R. Rao. Dordrecht, Netherlands: Elsevier. doi: 10.1016/B978-0-444-53859-8.00002-3

The cross-entropy method for estimation

2013

Book Chapter

Cross-entropy method

Kroese, Dirk P., Rubinstein, Reuven Y., Cohen, Izack, Porotsky, Sergey and Taimre, Thomas (2013). Cross-entropy method. Encyclopedia of operations research and management science. (pp. 326-333) edited by Saul I. Gass and Michael C. Fu. New York, United States: Springer. doi: 10.1007/978-1-4419-1153-7_131

Cross-entropy method

2013

Book Chapter

The cross-entropy method for optimization

Botev, Zdravko, I., Kroese, Dirk P., Rubinstein, Reuven Y. and L'Ecuyer, Pierre (2013). The cross-entropy method for optimization. Machine learning: theory and applications. (pp. 35-59) edited by Venu Govindaraju and C. R. Rao. Dordrecht, Netherlands: Elsevier. doi: 10.1016/B978-0-444-53859-8.00003-5

The cross-entropy method for optimization

2013

Book Chapter

Monte Carlo methods for portfolio credit risk

Brereton, Tim J., Kroese, Dirk P. and Chan, Joshua C. (2013). Monte Carlo methods for portfolio credit risk. Credit securitisations and derivatives: challenges for the global markets. (pp. 127-152) edited by Daniel Rösch and Harald Scheule. Chicester, United Kingdom: John Wiley & Sons. doi: 10.1002/9781118818503.ch7

Monte Carlo methods for portfolio credit risk

2012

Conference Publication

Efficient simulation of charge transport in deep-trap media

Brereton, Tim J., Kroese, Dirk P., Stenzel, Ole, Schmidt, Volker and Baumeier, Bjorn (2012). Efficient simulation of charge transport in deep-trap media. Winter Simulation Conference, Berlin, Germany, 9-12 December 2012. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WSC.2012.6465003

Efficient simulation of charge transport in deep-trap media

2012

Journal Article

Improved cross-entropy method for estimation

Chan, Joshua C.C. and Kroese, Dirk P. (2012). Improved cross-entropy method for estimation. Statistics and Computing, 22 (5), 1031-1040. doi: 10.1007/s11222-011-9275-7

Improved cross-entropy method for estimation

2012

Journal Article

Efficient Monte Carlo simulation via the generalized splitting method

Botev, Zdravko I. and Kroese, Dirk P. (2012). Efficient Monte Carlo simulation via the generalized splitting method. Statistics and Computing, 22 (1), 171-16. doi: 10.1007/s11222-010-9201-4

Efficient Monte Carlo simulation via the generalized splitting method

2012

Journal Article

Monte Carlo methods

Kroese, Dirk P. and Rubinstein, Reuven Y. (2012). Monte Carlo methods. Wiley Interdisciplinary Reviews: Computational Statistics, 4 (1), 48-58. doi: 10.1002/wics.194

Monte Carlo methods

2011

Journal Article

Rare-event probability estimation with conditional Monte Carlo

Chan, Joshua C. C. and Kroese, Dirk P. (2011). Rare-event probability estimation with conditional Monte Carlo. Annals of Operations Research, 189 (1), 43-61. doi: 10.1007/s10479-009-0539-y

Rare-event probability estimation with conditional Monte Carlo

2011

Journal Article

Preface

Kroese, Dirk, Shimkin, Namhum, Kreimer, Joseph and Juneja, Sandeep (2011). Preface. Annals of Operations Research, 189 (1), 1-3. doi: 10.1007/s10479-010-0745-7

Preface

2011

Journal Article

Stability and performance of greedy server systems: A review and open problems

Rojas-Nandayapa, Leonardo, Foss, Sergey and Kroese, Dirk P. (2011). Stability and performance of greedy server systems: A review and open problems. Queueing Systems: Theory and Applications, 68 (3-4), 221-227. doi: 10.1007/s11134-011-9235-0

Stability and performance of greedy server systems: A review and open problems

2011

Journal Article

The generalized cross entropy method, with applications to probability density estimation

Botev, Zdravko I. and Kroese, Dirk P. (2011). The generalized cross entropy method, with applications to probability density estimation. Methodology and Computing in Applied Probability, 13 (1), 1-27. doi: 10.1007/s11009-009-9133-7

The generalized cross entropy method, with applications to probability density estimation

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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