Fan Zhang

PhD Candidate
Teaching Assistant, Research Assistant
Worcester Polytechnic Institute

Address: 60 Prescott St, Worcester, MA 01605
Email: fzhang [at]
Google scholar profile
fzhang code on github

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.


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


Last updated 2016-06-03