Jeong E (Kate) Lee
Jeong E (Kate) Lee
Verified email at auckland.ac.nz
Title
Cited by
Cited by
Year
Bayesian inference on finite mixtures of distributions
K Lee, JM Marin, K Mengersen, C Robert
Perspectives in mathematical sciences I: Probability and statistics, 165-202, 2009
592009
Bayesian threshold selection for extremal models using measures of surprise
J Lee, Y Fan, SA Sisson
Computational Statistics & Data Analysis 85, 84-99, 2015
242015
Importance sampling schemes for evidence approximation in mixture models
JE Lee, CP Robert
Bayesian Analysis (In press) 11 (2), 573-597, 2015
122015
Population Monte Carlo algorithm in high dimensions
JE Lee, R McVinish, K Mengersen
Methodology and Computing in Applied Probability 13 (2), 369-389, 2011
82011
Calibration procedures for approximate Bayesian credible sets
JE Lee, G Nicholls, RJ Ryder
https://projecteuclid.org/euclid.ba/1570089912, 2019
62019
Weakly informative reparameterisations for location-scale mixtures
K Kamary, K Lee, C Robert
Journal of Computational and Graphical Statistics (in press), 2018
62018
A Loss-Based Prior for Variable Selection in Linear Regression Methods
C Villa, JE Lee
https://projecteuclid.org/euclid.ba/1560477728, 2019
5*2019
Influence of Resisted Sled-Pull Training on the Sprint Force-Velocity Profile of Male High-School Athletes
2 Cahill, Micheál J.1, 3 Oliver, Jon L.2, J Cronin, K Clark, MR Cross, ...
Journal of Strength and Conditioning Research 34, 2020
22020
Calibrated Approximate Bayesian Inference
G Nicholls, H Xing
ICML, http://proceedings.mlr.press/v97/xing19a, 2019
2*2019
Resisted Sprint Training in Youth; The Effectiveness of Backward vs. Forward Sled Towing on Speed, Jumping, and Leg Compliance Measures in High-School Athletes
A Uthoff, J Oliver, J Cronin, P Winwood, C Harrison, JE Lee
The Journal of Strength & Conditioning Research, 2019
22019
Ultimixt: Bayesian Analysis of a Non-Informative Parametrization for Gaussian Mixture Distributions
K Kaniav, K Lee
https://cran.r-project.org/web/packages/Ultimixt/, 2016
22016
Distortion estimates for approximate Bayesian inference
H Xing, G Nicholls, J Lee
The Conference on Uncertainty in Artificial Intelligence (UAI), 2020
12020
Bayesian hybrid algorithms and models: implementation and associated issues
JE Lee
Queensland University of Technology, 2010
12010
Posterior consistency for the spectral density of non-Gaussian stationary time series
Y Tang, C Kirch, JE Lee, R Meyer
arXiv preprint arXiv:2103.01357, 2021
2021
Tree based credible set estimation
JE Lee, GK Nicholls
arXiv preprint arXiv:2012.13837, 2020
2020
A Performance Comparison of Deep Learning Methods for Real-Time Localisation of Vehicle Lights in Video Frames
R Rapson, C., Seet, B.-C., Naeem, Naeem, M., Lee, J. and Klette
22nd Intelligent Transportation Systems Conference (ITSC), 2019
2019
Reducing the pain: A novel tool for efficient ground-truth labelling in image
R Rapson, C., Seet, B.-C., Naeem, M. A., Lee, J.E., Al-Sarayreh, M. and Klette
Image and Vision Computing New Zealand 2018, 2018
2018
Package ‘Ultimixt’
K Kamary, K Lee, MK Kamary, K MixReparametrized, ...
2015
Detecting de-lamination in composite beams using natural frequencies and the Bayesian inference
H Chung
The 22nd International Congress on Sound and Vibration. Florence, Italy, 2015
2015
Detecting defects in composite beams and plates using Bayesian inference
H Chung, J Lee
KU Leuven-Department of Mechanical Engineering, 2014
2014
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Articles 1–20