1. Allen, T. V., Singh, A., Greiner, R., & Hooper, P. (2008). Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference. Artificial Intelligence, 172(4–5), 483–513.
2. Babcock, B., & Chaudhuri, S. (2005). Towards a robust query optimizer: a principled and practical approach. In Proceedings of the 2005 ACM SIGMOD international conference on management of data. SIGMOD’05, New York, NY, USA (pp. 119–130). New York: ACM.
3. Bacchus, F. (1990). Representing and reasoning with probabilistic knowledge: a logical approach to probabilities. Cambridge: MIT Press.
4. Bacchus, F., Grove, A. J., Koller, D., & Halpern, J. Y. (1992). From statistics to beliefs. In AAAI (pp. 602–608).
5. Buchman, D., Schmidt, M. W., Mohamed, S., Poole, D., & de Freitas, N. (2012). On sparse, spectral and other parameterizations of binary probabilistic models. Journal of Machine Learning Research—Proceedings Track, 22, 173–181.