Author:
Sawczuk Thomas,Palczewska Anna,Jones Ben
Publisher
Springer International Publishing
Reference16 articles.
1. Ali, M., Deo, R., Downs, N., Maraseni, T.: Cotton yield prediction with Markov chain Monte Carlo-based simulation model integrated with genetic programming algorithm: a new hybrid copula-driven approach. Agric. Forest Meteorol. 263, 428–448 (2018). https://doi.org/10.1016/j.agrformet.2018.09.002
2. Buşoniu, L., Babus̆ka, R., De Schutter, B., Ernst, D.: Reinforcement Learning and Dynamic Programming Using Function Approximators. Taylor & Francis Group, Milton Park (2010)
3. Cervone, D., D’Amour, A., Bornn, L., Goldsberry, K.: A multiresolution stochastic process model for predicting basketball possession outcomes. J. Am. Stat. Assoc. 111(514), 585–599 (2016). https://doi.org/10.1080/01621459.2016.1141685
4. Girard, J., Emami, M.R.: Concurrent Markov decision processes for robot team learning. Eng. Appl. Artif. Intell. 39, 223–234 (2015). https://doi.org/10.1016/j.engappai.2014.12.007
5. Goldner, K.: A Markov model of football: Using stochastic processes to model a football drive. J. Quant. Anal. Sports 8, 1–18 (2012). https://doi.org/10.1515/1559-0410.1400