1. Ahmed, A., & Xing, E. P. (2009). Recovering time-varying networks of dependencies in social and biological studies. Proceedings of the National Academy of Sciences of the United States of America, 106(29), 11878–11883.
2. Bachmann, P., & Precup, D. (2012). Improved estimation in time varying models. In Proceedings of the 29th international conference on machine learning (ICML’12), pp. 1735–1742.
3. Banerjee, O., Ghaoui, L. E., & d’Aspremont, A. (2008). Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data. Journal of Machine Learning Research, 9, 485–516.
4. Bertsekas, D. P., & Tsitsiklis, J. N. (1989). Parallel and distributed computation: Numerical methods. Englewood Cliffs, NJ: Prentice-Hall.
5. Besag, J. (1975). Statistical analysis of non-lattice data. Journal of the Royal Statistical Society: Series D, 24(3), 179–195.