Stat 853 (4 credits) 2016                Dave Campbell

 
 

Papers and book chapters We Will Use (Still being Finalized):

All resources are available through the SFU library or online except the Highly Recommended Text.


Highly Recommended Text:

  1. Statistical Computing with R  by Maria L. Rizzo. Publisher: Chapman and Hall/CRC ISBN: 9781584885450


Recommended Texts (ebooks available from SFU library):

  1. Using R for Numerical Analysis in Science and Engineering (2014) by Victor R. Bloomfield. Publisher: Chapman and Hall/CRC, ISBN: 9781439884485


  1. Introduction to Scientific Programming and Simulation Using R (2014), 2nd ed. by Owen Jones, Robert Maillardet, and Andrew Robinson, ISBN: 9781466569997


Stat Software and Computing Concerns

  1. Baggerly, K A, and K R Coombes. Deriving Chemosensitivity From Cell Lines: Forensic Bioinformatics and Reproducible Research in High-throughput Biology. The Annals of Applied Statistics 3, no. 4 (2009): 1309-1334.


  1. Additional resources will be provided on software implementation details such as generating random numbers, setting random seeds, parallel computing, vectorization, numerical precision, and accessing the cluster


  1. Additional resources will be provided on useful software including article reference managers, Sweave, and /or Knitr


RStudio

  1. Make sure that you have R installed. Optionally you can install RStudio as well.  It's a nice, contained code and output experience that plays nice with LaTeX via the library knitr. RStudio And R are free. Rstudio is made by a group that is doing brilliant things for the Stat software community. If you see them at a conference be sure to give them high-5's.


LaTeX typesetting software

  1. Using LaTeX on a Mac MacTeX and the front end package TeXShop

  2. Alternatively use a Windows machine via pro-TeXt front end such as TeXstudio

  3. It's not hard to learn LaTeX, it is highly google-able.  Most people start from a template. Here's the last thing I LaTeX'd and the associated pdf.  From there it is all just details like adding figures and tables...


Optimization

  1. The book “Statistical Computing with R“ Sections 11.4 & 11.5


  1. Nash, J C, and R Varadhan. Unifying Optimization Algorithms to Aid Software System Users: Optimx for R. Journal of Statistical Software 43, no. 9 (2011): 1-14.


  1. Moles, C G, J R Banga, and K Keller. Solving Nonconvex Climate Control Problems: Pitfalls and Algorithm Performances. Applied Soft Computing 5, no. 1 (2004): 35-44.


  1. Wolpert, D H, and W G Macready. No Free Lunch Theorems for Optimization. Evolutionary Computation, IEEE Transactions on 1, no. 1 (1997): 67-82.


  1. Mebane Jr, W R, and J S Sekhon. "Genetic Optimization Using Derivatives: The Rgenoud Package for R." Journal of Statistical Software 42, no. 11 (2011): 1-26.


Bootstrap

  1. The book “Statistical Computing with R“ Chapter 7


  1. Givens, G. H., & Hoeting, J. A. (2005). Computational statistics (Vol. 483).Wiley-Interscience. Specifically we will use Chapters 11 and 13


  1. Lange, K. (2010). Numerical analysis for statisticians. In Numerical analysis for statisticians (2 ed.) Springer. Specifically we will use Chapter 24


EM algorithm

  1. The book “Statistical Computing with R“ Section 11.7.


  1. Lange, K. (2010). Numerical analysis for statisticians. In Numerical analysis for statisticians (2 ed.) Springer. Specifically we will use Chapter 13


Bayesian Techniques

  1. The book “Statistical Computing with R“ Chapter 9


  1. Geyer, C. (2010). Introduction to markov chain monte carlo. In S. Brooks, A. Gelman, G. L. Jones, & X. L. Meng (Eds.), Handbook of markov chain monte carlo: Methods and applications. Chapman & Hall/CRC.


  1. Cowles, Mary Kathryn, and Bradley, P Carlin. Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review. Journal of the American Statistical Association 91, no. 434 (1996): 883-904.


  1. Kass, R E, B P Carlin, A Gelman, and R M Neal. Markov Chain Monte Carlo in Practice: A Roundtable Discussion. American Statistician (1998): 93-100.


  1. Marin, J M, K Mengersen, and C P Robert. Bayesian Modelling and Inference on Mixtures of Distributions. Handbook of statistics 25 (2005): 459-507.


  1. Lesaffre, E., & Lawson, A. B. (2012). Bayesian biostatistics. Wiley. Specifically we will use Ch9 - Hierarchical Models


  1. Neal, R. M. (2003). Slice sampling (with discussion). Annals of Statistics, 31(3), 705-767


  1. Jasra, Ajay, David A Stephens, and Christopher C Holmes. On Population-Based Simulation for Static Inference. Statistics and Computing 17 (2007): 263-279.


  1. Neal, R. M. (2010). MCMC using hamiltonian dynamics. In S. Brooks, A. Gelman, G. L. Jones, & X. L. Meng (Eds.), Handbook of markov chain monte carlo: Methods and applications. Chapman & Hall/CRC.


  1. Girolami, M., & Calderhead, B. (2011). Riemannian manifold hamiltonian monte carlo. Journal of the Royal Statistical Society Series B, 73(2), 123-214.


  1. Gustafson, P. (2009). What are the limits of posterior distributions arising from nonidentified models and why should we care? Journal of the American Statistical Association, 104(488), 1682-1695.


  1. Reversible Jump Markov Chain Monte Carlo via http://cvlab.epfl.ch/~ksmith/tutorial/rjmcmc.php and other resources that I will add later


Methods for Intractable Likelihoods

  1. Wood, Simon. Statistical Inference for Noisy Nonlinear Ecological Dynamic Systems. Nature 466 (2010): doi:10.1038/nature09319.


  1. Ryder, R, C P Robert, P Pudlo, and J M Marin. Approximate Bayesian Computational Methods. Statistics and Computing (2011).


Large Scale Neural Network / Deep Learning

  1. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (Preliminary White Paper, November 9, 2015) Martín Abadi et al. http://download.tensorflow.org/paper/whitepaper2015.pdf


 

Stat 853: Statistical Computing

© Dave Campbell 2016