Biostatistics 615/815 Fall 2011

Objective

In Fall 2011, 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

• Recommended 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 M4332

Topics

The following contents are planned to be covered.

Part I : C++ Basics and Introductory Algorithms

• Computational Time Complexity
• Sorting
• Divide and Conquer Algorithms
• Searching
• Key Data Structure
• Dynamic Programming
• Hidden Markov Models

Part II : Numerical Methods and Randomized Algorithms

• Random Numbers
• Matrix Operations and Least Square Methods
• Importance Sampling
• Expectation Maximization
• Markov-Chain Monte Carlo Methods
• Simulated Annealing
• Gibbs Sampling

Class Notes

• Lecture 1 : Introduction to Statistical Computing -- (Handout PDF) (Presentation PDF)
• Lecture 2 : Introduction to C++ Programming -- (Handout PDF) (Presentation PDF)
• Lecture 3 : C++ Basics and Fisher's Exact Test -- (Handout PDF) (Presentation PDF)
• Lecture 4 : Standard Template Libraries + Divide and Conquer Algorithms-- (Handout PDF) (Presentation PDF)
• Lecture 5 : Sorting Algorithms -- (Handout PDF) (Presentation PDF)
• Lecture 6 : Sorting Algorithms & Arrays -- (Handout PDF) (Presentation PDF)
• Lecture 7 : List and Binary Search Trees -- (Handout PDF) (Presentation PDF)
• Lecture 8 : Hash Tables -- (Handout PDF) (Presentation PDF)
• Lecture 9 : Dynamic Programming -- (Handout PDF) (Presentation PDF)
• Review : Dynamic Programming & Midterm Review -- (PDF)
• Lecture 10 : Hidden Markov Model-- (Handout PDF) (Presentation PDF)
• Lecture 11 : Hidden Markov Model (cont'd) -- (Handout PDF) (Presentation PDF)

Problem Sets

• Problem Set 0 - Running screenshots of helloWorld.cpp and towerOfHanoi.cpp - Due before the submission of Problem Set 1
• Problem Set 1 -- Due on Tuesday September 27th, 2011 (PDF) (PDF-SOLUTIONS)
• Problem Set 2 -- Due on Thursday October 6th, 2011 (PDF) (PDF-SOLUTIONS)
  • (Example data - shuf-1M.txt.gz) 1,000,000 randomly shuffled data (gzipped)
  • (Example data - Rand-1M-3digits.txt.gz) 1,000,000 random data from 1 to 1,000]] (gzipped)
  • (Example data - Rand-50k.txt.gz) 50,000 random data from 1 to 1,000,000)]] (gzippd)
  • (Update Oct 2, 2011 : Note that the problem 1 and 3 are slightly updated for clarification)
  • (If you can't decompress the files above properly, use this alternative link by CLICKING HERE )
• Problem Set 3 -- Due on Tuesday November 1st, 2011 (PDF)
  • Example input data for problem 3-2

TIME	TOSS	Pr(F)	Pr(HB)	Pr(TB)	MLSTATE
1	T	0.9758	0.0068	0.0174	FAIR
2	H	0.9640	0.0312	0.0048	FAIR
3	H	0.9584	0.0341	0.0075	FAIR
4	T	0.9504	0.0091	0.0406	FAIR
5	T	0.9444	0.0118	0.0438	FAIR
6	H	0.9313	0.0582	0.0105	FAIR
7	H	0.9216	0.0663	0.0121	FAIR
8	T	0.9068	0.0358	0.0574	FAIR
9	H	0.8794	0.0672	0.0534	FAIR
10	T	0.8124	0.0316	0.1560	FAIR
11	T	0.7474	0.0699	0.1827	FAIR
12	H	0.5663	0.4101	0.0236	HEAD-BIASED
13	H	0.4432	0.5512	0.0056	HEAD-BIASED
14	H	0.3642	0.6325	0.0032	HEAD-BIASED
15	H	0.3164	0.6809	0.0027	HEAD-BIASED
16	H	0.2911	0.7063	0.0026	HEAD-BIASED
17	H	0.2840	0.7134	0.0026	HEAD-BIASED
18	H	0.2937	0.7033	0.0031	HEAD-BIASED
19	H	0.3215	0.6714	0.0071	HEAD-BIASED
20	H	0.3699	0.5879	0.0422	HEAD-BIASED
21	T	0.4269	0.2127	0.3604	TAIL-BASED
22	T	0.4257	0.2133	0.3610	TAIL-BASED
23	H	0.3642	0.5936	0.0422	HEAD-BIASED
24	H	0.3129	0.6800	0.0071	HEAD-BIASED
25	H	0.2828	0.7141	0.0031	HEAD-BIASED
26	H	0.2709	0.7263	0.0028	HEAD-BIASED
27	H	0.2751	0.7203	0.0046	HEAD-BIASED
28	H	0.2947	0.6840	0.0213	HEAD-BIASED
29	T	0.3214	0.5070	0.1716	HEAD-BIASED
30	H	0.2823	0.6290	0.0887	HEAD-BIASED

Office Hours

• Friday 9:00AM-10:30PM

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

• Winter 2011 Course Web Site Biostatistics_615/815_Winter_2011
• Goncalo Abecasis taught it in several academic years previously. For previous course notes, see [Goncalo's older class notes].