Biostatistics 615/815 Fall 2011

Objective

In this winter, Biostatistics 615/815 aims for providing students with a practical understanding of computational aspects in implementing statistical methods. Although C++ language will be used throughout the course, using Java programming language for homework and project will be acceptable.

Target Audience

Students in Biostatistics 615 should be comfortable with simple algebra and basic statistics including probability distribution, linear model, and hypothesis testing. Previous experience in programming is not required, but those who do not have previous programming experience should expect to spend additional time studying and learning to be familiar with a programming language during the coursework. Most students registering for the course are Masters or Doctoral students in Biostatistics, Statistics, Bioinformatics or Human Genetics.

Students in Biostatistics 815 should be familiar with programming languages so that they can complete the class project tackling an advanced statistical problem during the semester. Project will be carried out in teams of 2. The details of the possible projects will be announced soon.

Textbook

• Required Textbook : Cormen, Leiserson, Rivest, and Stein, "Introduction to Algorithms", Third Edition, The MIT Press, 2009 [Official Book Web Site]
• Optional Textbook : Press, Teukolsky, Vetterling, Flannery, "Numerical Recipes", 3rd Edition, Cambridge University Press, 2007 [Official Book Web Site]

Class Schedule

Classes are scheduled for Tuesday and Thursdays, 8:30 - 10:00 am at SPH II M4318

Topics

The following contents are planned to be covered. The details are subject to change without prior notice.

Part I : Algorithms 101

• Understanding of Computational Time Complexity
• Sorting
• Divide and Conquer Algorithms
• Searching
• Key Data Structure
• Dynamic Programming

Part II : Matrix Operations and Numerical Optimizations

• Matrix decomposition (LU, QR, SVD)
• Implementation of Linear Models
• Numerical Optimizations

Part III : Advanced Statistical Methods

• Hidden Markov Models
• Expectation Maximization
• Markov-Chain Monte Carlo Methods

Class Notes

• Lecture 1 : Statistical Computing -- (Handout mode - PDF) (Presentation mode - PDF)

Office Hours

• TBD

Standards of Academic Conduct

The following is an extract from the School of Public Health's Student Code of Conduct [1]:

Student academic misconduct includes behavior involving plagiarism, cheating, fabrication, falsification of records or official documents, intentional misuse of equipment or materials, and aiding and abetting the perpetration of such acts. The preparation of reports, papers, and examinations, assigned on an individual basis, must represent each student’s own effort. Reference sources should be indicated clearly. The use of assistance from other students or aids of any kind during a written examination, except when the use of books or notes has been approved by an instructor, is a violation of the standard of academic conduct.

In the context of this course, any work you hand-in should be your own.

Course History

Goncalo Abecasis taught it in several academic years previously. For previous course notes, see [Goncalo's older class notes].