Estimating the duration of professional tennis matches for varying formats SA Kovalchik, M Ingram Journal of Quantitative Analysis in Sports 14 (1), 13-23, 2018 | 22 | 2018 |
Multi‐output Gaussian processes for species distribution modelling M Ingram, D Vukcevic, N Golding Methods in ecology and evolution 11 (12), 1587-1598, 2020 | 20 | 2020 |
Hot heads, cool heads, and tacticians: Measuring the mental game in tennis (ID: 1464) S Kovalchik, M Ingram 10th Annual MIT Sloan Sports Analytics Conference, Boston, MA, 2016 | 20 | 2016 |
Adjusting bookmaker’s odds to allow for overround S Clarke, S Kovalchik, M Ingram American Journal of Sports Science 5 (6), 45-49, 2017 | 18 | 2017 |
A point-based Bayesian hierarchical model to predict the outcome of tennis matches M Ingram Journal of Quantitative Analysis in Sports 15 (4), 313-325, 2019 | 17 | 2019 |
How to extend Elo: a Bayesian perspective M Ingram Journal of Quantitative Analysis in Sports 17 (3), 203-219, 2021 | 8 | 2021 |
Space-time VON CRAMM: Evaluating decision-making in tennis with Variational generatiON of Complete Resolution Arcs via Mixture Modeling S Kovalchik, M Ingram, K Weeratunga, C Goncu arXiv preprint arXiv:2005.12853, 2020 | 6 | 2020 |
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box R Giordano, M Ingram, T Broderick Journal of Machine Learning Research 25 (18), 1-39, 2024 | 4 | 2024 |
Gaussian Process Priors for Dynamic Paired Comparison Modelling M Ingram arXiv preprint arXiv:1902.07378, 2019 | 1 | 2019 |
A Low-cost Spatiotemporal Data Collection System for Tennis SV Mora, G Barnett, CO da Costa-Luis, J Garcia, MI Maegli, J Neale, ... Proceedings of the conference on Visualization 1, 75-82, 0 | 1 | |
Scaling multi-species occupancy models to large citizen science datasets M Ingram, D Vukcevic, N Golding arXiv preprint arXiv:2206.08894, 2022 | | 2022 |
Approximate Bayesian inference for large-scale hierarchical modelling M Ingram The University of Melbourne, 2021 | | 2021 |