Introducing some computational techniques and algorithms used by statistical researchers and practitioners
1. Monte Carlo Methods (Integration and Optimization)
2. Markov Chain Monte Carlo
3. Resampling Methods (Bootstrap, Jackknife, and Cross-Validaton)
4. Data Mining (Trees, Neural Networks, and Support Vector Machines)

Introducing some computational techniques and algorithms used by statistical researchers and practicers
1. Monte Carlo Methods (Integration and Optimization)
2. Markov Chain Monte Carlo
3. Resampling Methods (Bootstrap, Jackknife, and Cross-Validaton)
4. Data Mining (Trees, Neural Networks, and Support Vector Machines)

a. Monte Carlo Statistical Methods by Christian P. Robert & George Casella
b. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman
c. Statistical Computing by William J. Kennedy & James E. Gentle
d. Elements of Statistical Computing by Ronald A. Thisted
e. Bayesian Statistical Modeling by Peter Congdon
f. An Introduction to the Bootstrap by Bradley Efron and Robert J. Tibshirani
g. Simulation by Sheldon M. Ross
h. An Introduction to Statistical Learning With Application in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

a. Monte Carlo Statistical Methods by Christian P. Robert & George Casella
b. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman
c. Statistical Computing by William J. Kennedy & James E. Gentle
d. Elements of Statistical Computing by Ronald A. Thisted
e. Bayesian Statistical Modeling by Peter Congdon
f. An Introduction to the Bootstrap by Bradley Efron and Robert J. Tibshirani
g. Simulation by Sheldon M. Ross
h. An Introduction to Statistical Learning With Application in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani