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Martin Ingram
Martin Ingram
KONUX GmbH
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Title
Cited by
Cited by
Year
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
222018
Multi‐output Gaussian processes for species distribution modelling
M Ingram, D Vukcevic, N Golding
Methods in ecology and evolution 11 (12), 1587-1598, 2020
202020
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
202016
Adjusting bookmaker’s odds to allow for overround
S Clarke, S Kovalchik, M Ingram
American Journal of Sports Science 5 (6), 45-49, 2017
182017
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
172019
How to extend Elo: a Bayesian perspective
M Ingram
Journal of Quantitative Analysis in Sports 17 (3), 203-219, 2021
82021
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
62020
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
42024
Gaussian Process Priors for Dynamic Paired Comparison Modelling
M Ingram
arXiv preprint arXiv:1902.07378, 2019
12019
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
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Articles 1–12