Biostatistics 615/815 Fall 2011

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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

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)
    • (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) (UPDATED on Oct 25th at 11:10AM)
  • Problem Set 4 -- Due on Tuesday November 15th, 2011 (PDF)
  • Problem Set 5 -- Due on Tuesday November 29th, 2011 (PDF)
  • Problem Set 6 -- Due on Tuesday December 13th, 2011 (PDF)

Supplementary Data sets for Problem Sets

TIME	TOSS	P(FAIR)	P(BIAS)	MLSTATE
1	H	0.5950	0.4050	FAIR
2	T	0.8118	0.1882	FAIR
3	H	0.8071	0.1929	FAIR
4	T	0.8584	0.1416	FAIR
5	H	0.7613	0.2387	FAIR
6	H	0.7276	0.2724	FAIR
7	T	0.7495	0.2505	FAIR
8	H	0.5413	0.4587	BIASED
9	H	0.4187	0.5813	BIASED
10	H	0.3533	0.6467	BIASED
11	H	0.3301	0.6699	BIASED
12	H	0.3436	0.6564	BIASED
13	H	0.3971	0.6029	BIASED
14	T	0.5028	0.4972	BIASED
15	H	0.3725	0.6275	BIASED
16	H	0.2985	0.7015	BIASED
17	H	0.2635	0.7365	BIASED
18	H	0.2596	0.7404	BIASED
19	H	0.2858	0.7142	BIASED
20	H	0.3482	0.6518	BIASED
    • Example output data for problem 3-2 (input is the second column) (NOTE : UPDATED on Oct 25 11:23PM)
TIME	TOSS	Pr(F)	Pr(HB)	Pr(TB)	MLSTATE
1	T	0.8844	0.0326	0.0830	FAIR
2	H	0.9012	0.0791	0.0198	FAIR
3	H	0.9075	0.0735	0.0189	FAIR
4	T	0.9091	0.0145	0.0764	FAIR
5	T	0.9068	0.0114	0.0818	FAIR
6	H	0.9058	0.0440	0.0502	FAIR
7	T	0.8834	0.0275	0.0891	FAIR
8	H	0.8520	0.0698	0.0783	FAIR
9	T	0.7713	0.0347	0.1940	FAIR
10	T	0.6927	0.0823	0.2249	FAIR
11	H	0.4730	0.4984	0.0286	HEAD-BIASED
12	H	0.3227	0.6706	0.0066	HEAD-BIASED
13	H	0.2236	0.7726	0.0037	HEAD-BIASED
14	H	0.1589	0.8381	0.0031	HEAD-BIASED
15	H	0.1169	0.8803	0.0028	HEAD-BIASED
16	H	0.0902	0.9072	0.0026	HEAD-BIASED
17	H	0.0740	0.9235	0.0025	HEAD-BIASED
18	H	0.0654	0.9321	0.0025	HEAD-BIASED
19	H	0.0630	0.9346	0.0025	HEAD-BIASED
20	H	0.0661	0.9314	0.0025	HEAD-BIASED
21	H	0.0755	0.9219	0.0026	HEAD-BIASED
22	H	0.0926	0.9038	0.0036	HEAD-BIASED
23	H	0.1204	0.8684	0.0113	HEAD-BIASED
24	H	0.1603	0.7586	0.0811	HEAD-BIASED
25	T	0.1904	0.0858	0.7238	TAIL-BASED
26	T	0.1819	0.0118	0.8063	TAIL-BASED
27	T	0.1797	0.0036	0.8167	TAIL-BASED
28	T	0.1894	0.0028	0.8077	TAIL-BASED
29	T	0.2136	0.0038	0.7826	TAIL-BASED
30	T	0.2561	0.0123	0.7317	TAIL-BASED
    • Example input/output data for problem 3-3 (Applying 2-state HMM in Problem 3-1): Download using THIS LINK

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