General Bibliography¶
- 1
Andrew Gelman and Jennifer Hill. Data analysis using regression and multilevel/hierarchical models. Cambridge university press, 2006.
- 2
Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, 2009. doi:10.1109/MC.2009.263.
- 3
F. Maxwell Harper and Joseph A. Konstan. The movielens datasets: history and context. January 2016. URL: https://doi.org/10.1145/2827872.
- 4
Andriy Mnih and Russ R Salakhutdinov. Probabilistic matrix factorization. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems, volume 20. Curran Associates, Inc., 2008. URL: https://proceedings.neurips.cc/paper/2007/file/d7322ed717dedf1eb4e6e52a37ea7bcd-Paper.pdf.
- 5
Steven J Nowlan and Geoffrey E Hinton. Simplifying neural networks by soft weight-sharing. Neural computation, 4(4):473–493, 1992.
- 6
Ruslan Salakhutdinov and Andriy Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proceedings of the 25th international conference on Machine learning, volume 25, 880–887. 2008.
- 7
Richard McElreath. Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC, 2018.
- 8
Daniela James, Gareth ad Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning. Springer, 2021. ISBN 978-1-0716-1420-4. doi:https://doi.org/10.1007/978-1-0716-1418-1.
- 9
Daniel Lewandowski, Dorota Kurowicka, and Harry Joe. Generating random correlation matrices based on vines and extended onion method. Journal of multivariate analysis, 100(9):1989–2001, 2009.
- 10
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, and David M. Blei. Automatic variational inference in stan. 2015. arXiv:1506.03431.
- 11
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. 2013. arXiv:1312.5602.
- 12
C. Maddison et al. D. Silver, A. Huang. Mastering the game of go with deep neural networks and tree search. Nature, 529:484–489, 2016. URL: https://doi.org/10.1038/nature16961.
- 13
Diederik P Kingma and Max Welling. Auto-encoding variational bayes. 2014. arXiv:1312.6114.
- 14
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. 2014. arXiv:1409.4842.
- 15
Andrew Gelman. Multilevel (hierarchical) modeling: what it can and cannot do. Technometrics, 48(3):432–435, 2006.
- 16
Ken Goldberg, Theresa Roeder, and Chris Perkins. Eigentaste: a constant time collaborative filtering algorithm. Information Retrieval, 4:133–151, 2001.
- 17
Leland Wilkinson. The Grammar of Graphics. Springer, 2005. ISBN 978-0-387-24544-7. doi:https://doi.org/10.1007/0-387-28695-0.