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)

101 - 120 of 138 works

2005

Conference Publication

Designing an optimal network using the cross-entropy method

Nariai, Sho, Hui, Kin-Ping and Kroese, Dirk P. (2005). Designing an optimal network using the cross-entropy method. Sixth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005), Brisbane, Australia, 6-8 July 2005. Heidelberg, Germany: Springer. doi: 10.1007/11508069_30

Designing an optimal network using the cross-entropy method

2005

Journal Article

Review of Kernel Methods for Pattern Analysis

Kroese, D. P. (2005). Review of Kernel Methods for Pattern Analysis. Siam Review, 47 (2), 385-387. doi: 10.1137/SIREAD000047000002000367000001

Review of Kernel Methods for Pattern Analysis

2005

Journal Article

Preface : From the issue entitled "The Cross-Entropy Method for Combinatorial Optimization, Rare Event Simulation and Neural Computation"

Kroese, Dirk P. and Rubinstein, Reuvem Y. (2005). Preface : From the issue entitled "The Cross-Entropy Method for Combinatorial Optimization, Rare Event Simulation and Neural Computation". Annals of Operations Research, 134 (1), 17-18. doi: 10.1007/s10479-005-5723-0

Preface : From the issue entitled "The Cross-Entropy Method for Combinatorial Optimization, Rare Event Simulation and Neural Computation"

2005

Journal Article

Application of the cross-entropy method to the buffer allocation problem in a simulation-based environment

Alon, G., Kroese, D. P., Raviv, T. and Rubinstein, R. Y. (2005). Application of the cross-entropy method to the buffer allocation problem in a simulation-based environment. Annals of Operations Research, 134 (1), 137-151. doi: 10.1007/s10479-005-5728-8

Application of the cross-entropy method to the buffer allocation problem in a simulation-based environment

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

2004

Journal Article

Erratum: A generalised Markov sampler (Methodology and Computing in Applied Probability 6:1 (29-53))

Keith, , Kroese, and Bryant, (2004). Erratum: A generalised Markov sampler (Methodology and Computing in Applied Probability 6:1 (29-53)). Methodology and Computing in Applied Probability, 6 (3). doi: 10.1023/B:MCAP.0000026605.65154.56

Erratum: A generalised Markov sampler (Methodology and Computing in Applied Probability 6:1 (29-53))

2004

Conference Publication

Global likelihood optimization via the cross-entropy method with an application to mixture models

Botev, Zdravko and Kroese, Dirk P. (2004). Global likelihood optimization via the cross-entropy method with an application to mixture models.

Global likelihood optimization via the cross-entropy method with an application to mixture models

2004

Journal Article

A fast cross-entropy method for estimating buffer overflows in queuing networks

De Boer, P. T., Kroese, D. P. and Rubinstein, R. Y. (2004). A fast cross-entropy method for estimating buffer overflows in queuing networks. Management Science, 50 (7), 883-895. doi: 10.1287/mnsc.1030.0139

A fast cross-entropy method for estimating buffer overflows in queuing networks

2004

Journal Article

A generalized Markov sampler

Keith, Jonathan M., Kroese, Dirk P. and Bryant, Darryn (2004). A generalized Markov sampler. Methodology And Computing In Applied Probability, 6 (1), 29-53. doi: 10.1023/B:MCAP.0000012414.14405.15

A generalized Markov sampler

2004

Book

The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning

Rubinstein, R.Y. and Kroese, D. P. (2004). The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. New York: Springer. doi: 10.1007/978-1-4757-4321-0

The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning

2004

Conference Publication

The transform likelihood ratio method for rare event simulation with heavy tails

Kroese, D. P. and Rubinstein, R. Y. (2004). The transform likelihood ratio method for rare event simulation with heavy tails. United States: Springer New York LLC. doi: 10.1023/B:QUES.0000027989.97672.be

The transform likelihood ratio method for rare event simulation with heavy tails

2004

Conference Publication

Global Likelihood Optimization Via The Cross-Entropy Method With An Application To Mixture Models

Botev, Z. I. and Kroese, D. P. (2004). Global Likelihood Optimization Via The Cross-Entropy Method With An Application To Mixture Models. 2004 Winter Simulation Conference, Washington, USA, 5-8 December, 2004. Washington: Board of Winter Simulation Conference.

Global Likelihood Optimization Via The Cross-Entropy Method With An Application To Mixture Models

2004

Journal Article

Spectral properties of the tandem Jackson network, seen as a quasi-birth-and-death process

Kroese, D. P., Scheinhardt, W. R. W. and Taylor, P. J. (2004). Spectral properties of the tandem Jackson network, seen as a quasi-birth-and-death process. Annals of Applied Probability, 14 (4), 2057-2089. doi: 10.1214/105051604000000477

Spectral properties of the tandem Jackson network, seen as a quasi-birth-and-death process

2004

Other Outputs

Heavy Tails, Importance Sampling and Cross-Entropy

Asmussen, S., Kroese, D. P. and Rubinstein, R. Y. (2004). Heavy Tails, Importance Sampling and Cross-Entropy.

Heavy Tails, Importance Sampling and Cross-Entropy

2003

Journal Article

The tree cut and merge algorithm for estimation of network reliability

Hui, KP, Bean, N, Kraetzl, M and Kroese, D (2003). The tree cut and merge algorithm for estimation of network reliability. Probability In The Engineering And Informational Sciences, 17 (1), 23-45. doi: 10.1017/S0269964803171021

The tree cut and merge algorithm for estimation of network reliability

2003

Conference Publication

Network reliability estimation using the tree cut and merge algorithm with importance sampling

Hui, K.-P., Bean, N.G., Kraetzl, M. and Kroese, D. P. (2003). Network reliability estimation using the tree cut and merge algorithm with importance sampling. DRCN2003, Banff, Canada, 19-22 October 2003. Canada: The Institute of Electrical & Electronics Engineers, Inc. doi: 10.1109/DRCN.2003.1275364

Network reliability estimation using the tree cut and merge algorithm with importance sampling

2002

Journal Article

On the importance function in splitting simulation

Garvels, Marnix J. J., van Ommeren, Jan- Kees C. W. and Kroese, Dirk P. (2002). On the importance function in splitting simulation. European Transactions on Telecommunications, 13 (4), 363-371. doi: 10.1002/ett.4460130408

On the importance function in splitting simulation

2002

Conference Publication

Sequence alignment by rare event simulation

Keith, Jonathan and Kroese, Dirk P. (2002). Sequence alignment by rare event simulation. 35th 2002 Winter Simulation Conference (ERA Rank B), San Diego, CA, U.S.A., 8-11 December 2002. United States: IEEE - Computer Society. doi: 10.1109/WSC.2002.1172901

Sequence alignment by rare event simulation

2002

Journal Article

Efficient simulation of a Tandem Jackson Network

Kroese, D. P. and Nicola, V. F. (2002). Efficient simulation of a Tandem Jackson Network. ACM Transactions on Modeling and Computer Simulation, 12 (2), 119-141. doi: 10.1145/566392.566395

Efficient simulation of a Tandem Jackson Network

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 Large and Complex Partially Observable Markov Decision Processes

    Associate Advisor

    Other advisors: Dr Nan Ye

  • Doctor Philosophy

    Reinforcement Learning for Partially Observable Environments

    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

    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

Completed supervision

Enquiries

Contact Professor Dirk Kroese directly for media enquiries about:

  • Monte Carlo simulation

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