Dr Kate Jeong Eun Lee

BTech (UoA), MSc (UoA), PhD (QUT)

Lecturer

In: Statistics » Faculty of Science

Research | Current

My primary research goals are directed toward understanding statistical inference and related computations in the Bayesian framework.

My research interests include;

Bayesian statsitics
Monte Carlo methods
Mixtures
Inference using approixmations due to expensive posteiror distribuitons
Inverse problem
Application of Bayesian inference - I have worked on problems in food science, sport science and image detection.

Please see Google scholar for my publications

https://scholar.google.co.nz/citations?user=B5R1SvgAAAAJ&hl=en

Teaching | Current

2019 S1 STATS 762

2019 S2 STATS 201/208 and STATS 331

Postgraduate supervision

PhD

2019-now, Yifu Tang

2019-now, Yixuan Liu

2015-2017, Mohammad Sazzad Mosharrof, “Sources of Uncertainties in Composite Structures; Theoretical and Computational Methods”

2012-2014, Sakthithasan Sripirakas, “High speed data stream mining using forest of decision tree”

Areas of expertise

Bayesian analysis, Monte Carlo method, Mixtures, Extremes

Committees/Professional groups/Services

International Society of Bayesian Analysis
New Zealand Statistical Association

Review panel of ICML, NIPS, AISTATS

Selected publications and creative works (Research Outputs)

Rousseau, J., Grazian, C., & Lee, J. E. (2019). Bayesian mixture models: Theory and methods. In S. Fruhwirth-Schnatter, G. Celeux, C. P. Robert (Eds.) Handbook of mixture analysis (pp. 55-75). Boca Raton, Florida, USA: Chapman and Hall/CRC. Related URL.
URL: http://hdl.handle.net/2292/45273
Kamary, K., Lee, J. E., & Robert, C. P. (2018). Weakly Informative Reparameterizations for Location-Scale Mixtures. Journal of Computational and Graphical Statistics, 1-13. 10.1080/10618600.2018.1438900
Rapson, C. J., Seet, B. C., Naeem, M. A., Lee, J. E., Al-Sarayreh, M., & Klette, R. (2018). Reducing the pain: A novel tool for efficient ground-truth labelling in images. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) Auckland, New Zealand: IEEE. 10.1109/IVCNZ.2018.8634750
URL: http://hdl.handle.net/2292/45325
Kamary, K., & Lee, K. (2017). Ultimixt: Bayesian analysis of location-scale mixture models using a weakly informative prior [Computer software]. Related URL.
URL: http://hdl.handle.net/2292/45448
Lee, J. E., & Robert, C. P. (2016). Importance Sampling Schemes for Evidence Approximation in Mixture Models. Bayesian Analysis, 11 (2), 573-597. 10.1214/15-BA970
Lee, J., Fan, Y., & Sisson, S. A. (2015). Bayesian threshold selection for extremal models using measures of surprise. Computational Statistics & Data Analysis, 85, 84-99. 10.1016/j.csda.2014.12.004
Chung, H., & Lee, K. (2015). Detecting de-lamination in composite beams using natural frequencies and the Bayesian inference. In M. J. Crocker, M. Pawelczyk, F. Pedrielli, E. Carletti, S. Luzzi (Eds.) 22nd International Congress on Sound and Vibration 2015 (ICSV 22), 2. Florence, Italy. Related URL.
URL: http://hdl.handle.net/2292/45487
Chung, H., & Lee, K. (2014). Detecting defects in composite beams and plates using Bayesian inference. In P. Sas, D. Moens, H. Denayer (Eds.) Proceedings International Conference on Noise and Vibration Engineering, International Conference on Uncertainly in Structural Dynamics, 3727-3738. Leuven, Belgium: Katholieke Universiteit Leuven. Related URL.
URL: http://hdl.handle.net/2292/45346