[1] D. Barber and F. Agakov. The IM algorithm: a variational approach to information maximization. NIPS, 16:201, 2004.
[2] M. I. Belghazi, A. Baratin, S. Rajeshwar, S. Ozair, Y. Bengio, A. Courville, and D. Hjelm. Mutual information neural estimation. In International conference on machine learning, pages 531-540. PMLR, 2018.
[3] J. M. Bernardo. Expected Information as Expected Utility. Ann. Stat., 7(3):686-690, May 1979.
[4] A. Bhrawy, E. Doha, S. Ezz-Eldien, and M. Abdelkawy. A numerical technique based on the shifted legendre polynomials for solving the time-fractional coupled kdv equation. *Calcolo*, 53, 01 2015. doi: 10.1007/s10092-014-0132-x.
[5] D. Blackwell. Comparison of experiments. In J. Neyman, editor, 2nd BSMSP, pages 93-102, Berkeley, CA, August 1950. UC Berkeley.
[6] G. Brown, A. Pocock, M.-J. Zhao, and M. Luján. Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. The journal of machine learning research, 13:27-66, 2012.
[7] A. Carrington, P. Fieguth, and H. Chen. Measures of model interpretability for model selection. In A. Holzinger, P. Kieseberg, A. M. Tjoa, and E. Weippl, editors, Machine Learning and Knowledge Extraction, pages 329–349, Cham, 2018. Springer International Publishing. ISBN 978-3-319-99740-7.
[8] T. M. Cover and J. A. Thomas. Elements of Information Theory 2nd Edition (Wiley Series in Telecommunications and Signal Processing). Wiley-Interscience, July 2006. ISBN 0471241954.
[9] M. D. Donsker and S. S. Varadhan. Asymptotic evaluation of certain markov process expectations for large time, i. Communications on Pure and Applied Mathematics, 28(1):1-47, 1975.
[10] A. Foster, M. Jankowiak, E. Bingham, P. Horsfall, Y. W. Teh, T. Rainforth, and N. Goodman. Variational bayesian optimal experimental design. In Advances in Neural Information Processing Systems 32, pages 14036-14047. 2019.
[11] S. Gershman, M. D. Hoffman, and D. M. Blei. Nonparametric variational inference. CoRR, abs/1206.4665, 2012.
[12] J. Goldberger, S. Gordon, H. Greenspan, et al. An efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures. In ICCV, volume 3, pages 487-493, 2003.
[13] J. M. Hernández-Lobato, M. W. Hoffman, and Z. Ghahramani. Predictive entropy search for efficient global optimization of black-box functions. Advances in neural information processing systems, 27, 2014.
[14] J. R. Hershey and P. A. Olsen. Approximating the kullback leibler divergence between gaussian mixture models. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, volume 4, pages IV-317-IV-320, 2007. doi: 10.1109/ICASSP.2007.366913.
[15] M. F. Huber, T. Bailey, H. Durrant-Whyte, and U. D. Hanebeck. On entropy approximation for gaussian mixture random vectors. In 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pages 181–188, 2008. doi: 10.1109/MFI.2008.4648062.
[16] S. Julier and J. K. Uhlmann. A general method for approximating nonlinear transformations of probability distributions. robotics research group, department of engineering science, university of oxford, 1996.
[17] D. V. Lindley. On a measure of the information provided by an experiment. The Annals of Mathematical Statistics, 27(4):986-1005, December 1956. ISSN 0003-4851.