This reading list is very helpful to me in doing research in developing statistical methods for large-scale genomic data analysis.
Among these, I read some chapters of some books several times because they are highly related to my research.

Note that this list is influenced by

Prof. Flaherty's list and

Mike Jordan's list .

**~ Enjoy your reading! ~**
**Probability and Statistics**
**Frequentist Statistics**
Casella, G. and Berger, R.L. (2001). "Statistical Inference" Duxbury Press.

Ferguson, T. (1996). "A Course in Large Sample Theory" Chapman & Hall/CRC.

Lehmann, E. (2004). "Elements of Large-Sample Theory" Springer provides an introduction to asymptotics.

Vaart, A.W. van der (1998). “Asymptotic Statistics” Cambridge ties together a lot of frequentist statistics (M-estimators, U-statistics, etc)
using empirical process theory.

B. Efron (2010) “Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction” Cambridge.

**Bayesian Statistics**
Gelman, A. et al. (2003). "Bayesian Data Analysis" Chapman & Hall/CRC.

Robert, C. and Casella, G. (2005). "Monte Carlo Statistical Methods" Springer.

Berger, J. and Wolpert, R (1988). "The likelihood principle: a review, generalizations, and statistical implications" IMS Lecture Notes
Monogr. Ser.

**Probability**
Grimmett, G. and Stirzaker, D. (2001). "Probability and Random Processes" Oxford.

Pollard, D. (2001). "A User's Guide to Measure Theoretic Probability" Cambridge.

Durrett, R. (2005). "Probability: Theory and Examples" Duxbury is the standard text book on probability.

Cover, T. and Thomas, J. "Elements of Information Theory" Wiley.

**Linear Algebra and Optimization**
**Convex Optimization**
D. and Tsitsiklis, J. (1997). "Introduction to Linear Optimization".

Boyd, S. and Vandenberghe, L. (2004). "Convex Optimization" Cambridge.

Golub, G., and Van Loan, C. (1996). "Matrix Computations" Johns Hopkins.

**Machine Learning**
Bishop C (2007) "Pattern Recognition and Machine Learning" Springer

**Note: inference and graphical models and Bayesian networks are available in Chapters 8-14.**
Murphy K (2012) "Machine Learning: A Probabilistic Perspective"

Hastie T and Tibshirani R. (2011) "The Elements of Statistical Learning: Data Mining, Inference, and Prediction"

Airoldi, EM (2014) "Handbook of Mixed Membership Models and Their Applications" CRC Press.

**Applied Statistical Genetics**
Lynch M and Walsh B. "Genetic Analysis of Quantitative Traits".

Neale, BM "Statistical Genetics: Gene-mapping through Linkage and Association".

**Computer Science**
TO BE ADDED.

Last updated 2016-06-03