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Painting of Zdravko

Zdravko Botev

Senior Lecturer in Computational Statistics and Applied Probability

School of Mathematics and Statistics
University of New South Wales
Sydney, NSW 2052
Australia

office: room 1034, The Red Center
secretary tel: (61) (02) 9385 7111
secretary fax: (61) (02) 9385 7123
email: botev(special symbol)unsw.edu.au


D. P. Kroese, Z. I. Botev, T. Taimre and R. Vaisman
Data Science and Machine Learning: Mathematical and Statistical Methods.
Chapman & Hall/CRC, 2019

Book Home Page:
https://github.com/DSML-book/

Order Information:
[ CRC Press | Amazon ]



Truncated Multivariate Student & Normal Toolbox

D. P. Kroese, T. Taimre, Z. I. Botev
Handbook of Monte Carlo Methods.
John Wiley & Sons, 2011

Handbook Home Page: montecarlohandbook.org

Order Information:
[ Wiley | Amazon ]

Statistical Software/Logiciels d'analyse Statistique

  • Kernel Density Estimator for One-dimensional Data in R: kde.R
  • Kernel Density Estimator for One-dimensional Data:      m file in compressed format: zip      m file: m
  • Bivariate Kernel Density Estimator:      m file in compressed format: zip      m file: m
  • Fast Multivariate Kernel Density Estimator for High Dimensions:      m file in compressed format: akde.zip      m file: akde.m
  • Stationary Gaussian Process:      m file in compressed format: zip      m file: m
  • Fractional Brownian Field:      m file in compressed format: zip      m file: m
  • Fractional Brownian Motion Generator:      m file in compressed format: zip      m file: m
  • Normal Quantile with Precision:      m file in compressed format: zip      m file: m
  • Multivariate normal cumulative distribution (QMC):      m file in compressed format: zip      m file: m
  • Multivariate normal cumulative distribution:      m file in compressed format: zip      m file: m
  • Truncated Normal Generator:      m file in compressed format: zip      m file: m
  • Truncated Multivariate Normal Generator:      m file in compressed format: zip      m file: m
  • Truncated Normal and Student's t-distribution toolbox:      Matlab toolbox: toolbox

Research Works

  • M. Colbrook, Z. I. Botev, K. Kuritz and S. MacNamara (2020), Kernel Density Estimation with Linked Boundary Conditions, Studies in Applied Mathematics, http://dx.doi.org/10.1111/sapm.12322
  • Z. I. Botev, P. L'Ecuyer (2020), Sampling Conditionally on a Rare Event via Generalized Splitting, INFORMS Journal on Computing, Article number: ijoc.2019.0936, https://pubsonline.informs.org/doi/abs/10.1287/ijoc.2019.0936
  • N. B. Rached, D. Mackinley, Z. I. Botev, R. Tempone, S. Alouini (2020), A Universal Splitting Estimator for the Performance Evaluation of Wireless Communications Systems, IEEE Transactions on Wireless Communications, pp. 1 - 10, http://dx.doi.org/10.1109/TWC.2020.2982649
  • D. P. Kroese, Z. I. Botev, T. Taimre, S. Vaisman (2019), Data Science and Machine Learning: Mathematical and Statistical Methods, Chapman & Hall/CRC Press, Book
  • T. Taimre, D. P. Kroese, and Z. I. Botev (2019), Monte Carlo Methods, Wiley StatsRef: Statistical Reference Online. John Wiley & Sons, Ltd.,http://dx.doi.org/10.1002/9781118445112.stat03619.pub2
  • Z. Botev; R. Salomone; D. Mackinlay, (2019), 'Fast and Accurate Computation of the Distribution of Sums of Dependent Log-Normals', Annals of Operations Research, http://dx.doi.org/10.1007/s10479-019-03161-x
  • Patrick J. Laub; Robert Salomone; Zdravko I. Botev, (2019), 'Monte Carlo estimation of the density of the sum of dependent random variables', Mathematics and Computers in Simulation, Vol. 161, July 2019, pp. 23-31, http://dx.doi.org/10.1016/j.matcom.2018.12.001
  • D. Mackinlay and Z. Botev, (2019), 'Mosaic Style Transfer Using Sparse Autocorrelograms', in Proceedings of the 20th ISMIR Conference, Delft, Netherlands, November 4-8, 2019, Delft, The Netherlands, presented at 20th annual conference of the International Society for Music Information Retrieval (ISMIR), Delft, The Netherlands, 04 November 2019 - 08 November 2019, https://ismir2019.ewi.tudelft.nl/?q=node/1
  • Z. I. Botev, Yi-Lung Chen, P. L'Ecuyer, S. MacNamara and D. P. Kroese (2018), Exact Posterior Simulation From The Linear Lasso Regression, Proceedings of the 2018 Winter Simulation Conference, pp. 1706 - 1717, Gothenburg, Sweden, 09 December 2018 - 12 December 2018, http://dx.doi.org/10.1109/WSC.2018.8632237
  • P. L'Ecuyer, Z. I. Botev and D. P. Kroese (2018), On a Generalzied Splitting Method For Sampling From a Conditional Distribution, Proceedings - 2018 Winter Simulation Conference, pp. 1694 - 1705, Gothenburg, Sweden, 09 December 2018 - 12 December 2018, http://dx.doi.org/10.1109/WSC.2018.8632422
  • Z. I. Botev and P. L'Ecuyer (2017), Simulation from the Tail of the Univariate and Multivariate Normal Distribution, in Innovations in Communications and Computing, edited by A. Puliafito and K. Trivedi, Chapter 8, Springer
  • Z. I. Botev, A. Ridder (2017), Variance Reduction , in Balakrishnan N; Colton T; Everitt B; Piegorsch W; Ruggeri F; Teugels JL (ed.), Wiley StatsRef: Statistics Reference Online, Wiley Online Library, pp. 1 - 6, http://dx.doi./10.1002/9781118445112.stat07975
  • N. B. Rached, Z. I. Botev, A. Kammoun, S. Alouini, R. Tempone (2018), Importance sampling estimator of outage probability under generalized selection combining model, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 3909 - 3913, Calgary, AB, Canada, 15 April - 20 April 2018, http://dx.doi.org/10.1109/ICASSP.2018.8462177
  • N. B. Rached, Z. I. Botev, A. Kammoun, S. Alouini, R. Tempone (2018), On the Sum of Order Statistics and Applications to Wireless Communication Systems Performances, IEEE Transactions on Wireless Communications, vol. 17, pp. 7801 - 7813, http://dx.doi.org/10.1109/TWC.2018.2871201
  • Z. I. Botev and P. L'Ecuyer (2017), Accurate Computation of The Right Tail of The Sum of Dependent Log-normal variates, in Chan WKV; D'Ambrogio A; Zacharewicz G; Mustafee N; Wainer G; Page E (eds.), 2017 Winter Simulation Conference (WSC), IEEE, pp. 1880 - 1890, Las Vegas, NV, USA, 03 December - 06 December 2017, http://dx.doi.org/10.1109/WSC.2017.8247924
  • Z. I. Botev and D. Mackinlay and Y.-L. Chen (2017), Logarithmically Efficient Estimation of the Tail of the Multivariate Normal Distribution, in Chan WKV; D'Ambrogio A; Zacharewicz G; Mustafee N; Wainer G; Page E (eds.), 2017 Winter Simulation Conference (WSC), IEEE, pp. 1903 - 1913, Las Vegas, NV, USA, 03 December - 06 December 2017, http://dx.doi.org/10.1109/WSC.2017.8247926
  • Z. I. Botev and P. L'Ecuyer (2016), Simulation from the Normal Distribution Truncated to an Interval in the Tail, in ValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools, pp. 23 - 29, Taormina, Italy, 25th-28th Oct, 2016, http://dx.doi.org/10.4108/eai.25-10-2016.2266879
  • Z. I. Botev and Ad Ridder (2016), An M-estimator for rare-event probability estimation, in T. M. K. Roeder, P. I. Frazier, R. Szechtman, and E. Zhou, eds. Proceedings of the 2016 Winter Simulation Conference, IEEE Press Piscataway, pp. 359 - 369, Arlington, Virginia, USA, 11 December - 14 December 2016, http://dx.doi.org/10.1109/WSC.2016.7822103
  • Z. I. Botev (2016), The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting, Journal of the Royal Statistical Society, Series B (Statistical Methodology), Vol. 79, pp. 125-148, http://dx.doi.org/10.1111/rssb.12162
  • Z. I. Botev, M. Mandjes and A. Ridder (2015), Tail distribution of the Maximum of Correlated Gaussian Random Variables, in Yilmaz, L; Chan WKV; Moon I; Roeder TMK; Macal C; Rossetti, MD (eds.), Proceedings of the 2015 Winter Simulation Conference, IEEE, pp. 633 - 642, Huntington Beach, CA, 06 December 2015 - 09 December 2015, http://dx.doi.org/10.1109/WSC.2015.7408202
  • Z. I. Botev and P. L'Ecuyer (2015), Efficient Estimation and Simulation of the Truncated Multivariate Student-t Distribution, in Yilmaz, L; Chan WKV; Moon I; Roeder TMK; Macal C; Rossetti, MD (eds.), Proceedings of the 2015 Winter Simulation Conference, IEEE, pp. 380 - 391, Huntington Beach, CA, 06 December 2015 - 09 December 2015, http://dx.doi.org/10.1109/WSC.2015.7408180
  • R. Vaisman, Z. I. Botev, A. Ridder (2015), Sequential Monte Carlo for Counting Vertex Covers in General Graphs, Statistics and Computing, vol. 26, pp. 591 - 607, http://dx.doi.org/10.1007/s11222-015-9546-9
  • Z. I. Botev, C. J. Lloyd (2015), Importance accelerated Robbins-Monro algorithm with applications to parametric confidence limits, Electronic Journal of Statistics, Volume 9, Number 2, pages 2058-2075, http://dx.doi.org/10.1214/15-EJS1071
  • Z. I. Botev, A. Ridder, L. Rojas-Nandayapa (2016), Semiparametric Cross Entropy For Rare-Event Simulation, Journal of Applied Probability ,Volume 53, pp. 633 - 649, http://dx.doi.org/10.1017/jpr.2016.31
  • Z. I. Botev, P. L'Ecuyer, R. Simard, B. Tuffin (2015), Static Network Reliability Estimation Under the Marshall-Olkin Copula, ACM Transactions on Modeling and Computer Simulation, vol. 26, http://dx.doi.org/10.1145/2775106
  • Z. I. Botev, S. Vaisman, R. Y. Rubinstein, P. L'Ecuyer (2014), Reliability of Stochastic Flow Networks with Continuous Link Capacities, in Tolk A; Diallo SY; Ryzhov IO; Yilmaz L; Buckley S; Miller JA (eds.), Proceedings of the 2014 Winter Simulation Conference, IEEE, pp. 543 - 552, Savannah, Georgia, USA, 07 December - 10 December 2014, http://dx.doi.org/10.1109/WSC.2014.7019919
  • D. P. Kroese, T. Brereton, T. Taimre, Z. I. Botev (2014), Why the Monte Carlo Method is so important today Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 6, pp. 386 - 392, http://dx.doi.org/10.1002/wics.1314
  • Z. Botev, P. L'Ecuyer, and B. Tuffin (2013), Modeling and Estimating Small Unreliabilities for Static Networks with Dependent Components, in Caruge D; Calvin C; Diop CM; Malvagi F; Trama J-C (eds.), Proceedings of SNA&MC 2013: Supercomputing in Nuclear Applications and Monte Carlo, Paris, France, 27 October - 31 October 2013, pp. Article #03306, http://dx.doi.org/10.1051/snamc/201403306
  • D. P. Kroese and Z. I. Botev (2013), Spatial Process Generation, in Schmidt V (ed.), Lectures on Stochastic Geometry, Spatial Statistics and Random Fields, Volume II: Analysis, Modeling and Simulation of Complex Structures, Springer-Verlag, Berlin, pp. 369 - 404, http://dx.doi.org/10.1007/978-3-319-10064-7_12
  • Z. I. Botev, P. L'Ecuyer, and B. Tuffin (2012), Dependent Failures in Highly-Reliable Static Networks, Proceedings of the 2012 Winter Simulation Conference, IEEE Press, Winter Simulation Conference, Berlin, Germany, pp. Article 39 (12pp), 09 December - 12 December 2012, http://dx.doi.org/10.1109/WSC.2012.6465033
  • Z. I. Botev, P. L'Ecuyer, and B. Tuffin (2012), Markov chain importance sampling with applications to rare event probability estimation, Statistics and Computing, , vol. 23, pp. 271 - 285, http://dx.doi.org/10.1007/s11222-011-9308-2
  • Z. I. Botev, P. L'Ecuyer, G. Rubino, R. Simard, and B. Tuffin (2012), Static Network Reliability Estimation via Generalized Splitting, INFORMS Journal on Computing, vol. 25, pp. 56 - 71, http://dx.doi.org/10.1287/ijoc.1110.0493
  • Z. I. Botev, D. P. Kroese, R. Y. Rubinstein, and P. L'Ecuyer (2012), The Cross-Entropy Method for Optimization, In Handbook of Statistics , Volume 31: Machine Learning. V. Govindaraju and C.R. Rao, Eds, North Holland.
  • Z. I. Botev, P. L'Ecuyer, and B. Tuffin (2011), An Importance Sampling Method Based on a One-Step Look-Ahead Density from a Markov Chain, Proceedings of the 2011 Winter Simulation Conference, IEEE Press,
  • D. P. Kroese, T. Taimre and Z. I. Botev (2011), Handbook of Monte Carlo Methods, John Wiley & Sons , [ Wiley, homepage]
  • Botev. Z.I. and Kroese D. P. (2010). Efficient Monte Carlo simulation via the Generalized Splitting Method. Statistics and Computing. DOI: 10.1007/s11222-010-9201-4
  • Botev. Z.I., Grotowski J.F and Kroese D. P. (2010). Kernel density estimation via diffusion. Annals of Statistics. Volume 38, Number 5, Pages 2916--2957
  • Botev, Z.I., Kroese, D.P. (2009). The Generalized Cross Entropy Method, with Applications to Probability Density Estimation. Methodology and Computing in Applied Probability. DOI: 10.1007/s11009-009-9133-7
  • Botev, Z.I., Kroese, D.P. (2008). Non-asymptotic bandwidth selection for density estimation of discrete data. Methodology and Computing in Applied Probability. Volume 10, Number 3, 435-451,
  • Botev, Z.I., Kroese, D.P. (2008). An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting. Methodology and Computing in Applied Probability. Volume 10, Number 4, 471-505 (pdf)
  • D. P. Kroese, T. Taimre, Z. I. Botev and R. Y. Rubinstein (2007), Solutions Manual for Monte Carlo Methods, Wiley-Interscience, Second edition [ Wiley | Amazon | Barnes and Noble ]
  • Botev, Z.I, Kroese, D.P., Taimre, T. (2007). Generalized Cross-Entropy Methods with Applications to Rare-Event simulation and Optimization. Simulation. Volume 83, Number 11, 785-806
  • Botev, Z.I, Kroese, D.P., Taimre, T. (2006). Generalized Cross-Entropy Methods. Proceedings of RESIM 2006, Bamberg, Germany, 1-30. (pdf)
  • Botev, Z., Kroese, D.P. (2004). Global Likelihood Optimization via the Cross-Entropy Method, with an Application to Mixture Models. Proceedings of the Winter Simulation Conference, Washington DC, pp 529--535. (pdf)
  • Bachelor of Science Honors Project
  • This paper has been used to help write the kernel density estimation software.

Professional Service and Engagement

  • Associate Editor, INFORMS Journal of Computing
  • Associate Editor, Australian & New Zealand Journal of Statistics
  • Steering Committee member for the Monte Carlo Methods Conference since 2019
  • Plenary Speaker at the MCM 2021 to be held at Universit├Ąt Mannheim in Germany in 5 - 9 July, 2021
  • Chair of organizing committee, 12-th International Conference on Monte Carlo Methods and Applications, July 2019, MCM2019
  • Scientific Committee member, 13-th International Conference in Monte Carlo & Quasi-Monte Carlo in Scientific Computing, July 2018
  • Program Committee member 11-th International Conference on Monte Carlo Methods and Applications, July 2017
  • Treasurer of the Australian Mathematical Society Applied Probability Special Interest Group from 2016 till 2020
  • Scientific Committee member, 8-th International Workshop on Applied Probability (IWAP2016), 2016
  • Co-chair, Analysis Methodology track, Winter Simulation Conference, December 2016, Washington, D.C.

Student Supervision

  • (Honours degree, 2020) Rui Tong, Carl Yang
  • (Masters degree, 2020) Kevin Lam, Tianran Hu
  • (Honours degree, 2019 February) Marcus Berkley, Anant Mathur, Andy Bao, Ni Tao
  • (Masters degree, 2018 March) Yuhui Xia
  • (Honours degree, 2017 March) Brendon Lai
  • (PhD degree, 2017 March) Yi-Lung Chen
  • (PhD degree, March 2017) Kimie Suzuki Morales (non-primary supervisor)
  • (PhD degree, commenced 2016 March) Daniel Mackinlay
  • (2016 Honours degree) Yi-Lung Chen, Nallainathan Pradeesh
  • (2015 Honours degree)
    Kevin Lam, The Dynamic Splitting Method with an Application to Portfolio Credit Risk
    Vivian Zhong, Auxiliary Variable Methods in Bayesian Inference,
    Xuebin Zheng, Perfect Simulation from the Constrained and Censored Bayesian Linear Regression Models
  • (2014 Honours degree) Albert Lam, MultiLevel Monte Carlo Applications to Option Pricing
  • (2014 Special Topic) Adam Nie, Variational Bounds on Multivariate Normal Probabilities and an Importance Sampling Scheme
  • (2014 Vacation Scholars)
    Matthew Goodwin
    James Yang
    Adam Nie
    Yi-Lung Chen
    Fadi Antown
  • (2013 Honours degree) Alvin Huang, Rare-event probability Estimation via Empirical Likelihood Maximization
  • (2012 Master degree) Nancy Glass, Multilevel Monte Carlo method with applications to financial derivatives pricing.

Teaching