**1 Probability Theory**

[1] Jeffreys, H. (1983). Theory of Probability. Oxford University Press, Oxford, New York.

[2] Sivia, D.S., and Skilling, J. (2006). Data Analysis: A Bayesian Tutorial. Oxford University Press, New York.

[3] Ang, A.H.-S., and Tang, W.H. (2007). Probability concepts in engineering, Vol. I Emphasis on applications to civil and environmental engineering, 2nd Ed. Wiley, New York.

[4] Beck, J.L. (2010). Bayesian system identification based on probability logic. Structural Control and Health Monitoring, 17(7), 825847.

[5] Yuen, K.V. (2010). Bayesian Methods for Structural Dynamics and Civil Engineering. John Wiley & Sons, ISBN: 978-0-470-82454-2.

[6] Gelman, A., et al. (2013). Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC.

**2 Information Theory**

[7] Cover, T. M. and J. A. Thomas (1991). Elements of Information Theory. Wiley.

[8] MacKay, D. J. C. (2003). Information Theory, Inference and Learning Algorithms. Cambridge University Press.

**3 Decision Theory**

[9] Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis (Second ed.). Springer.

[10] Bather, J. (2000). Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions. Wiley.

**4 Introductory Materials on Machine Learning**

[11] Mitchell, T. (1997). Machine Learning. McGraw Hill.

[12] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer-Verlag New York.

[13] Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.

[14] Farrar, C. R., and Worden, K. (2012). Structural health monitoring: a machine learning perspective. John Wiley and Sons.

[15] Alpaydin, E. (2014). Introduction to Machine Learning, Third Edition. The MIT Press, London.