615.01 - Biostatistics 615/815 Statistical Computing Gonalo...

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Biostatistics 615/815 Statistical Computing Gonçalo Abecasis
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Course Objective z Introduce skills required for executing statistical computing projects z Applications and examples mostly in C. Can be easily translated into R, etc. z But the focus is on an algorithmic way of thinking!
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Part I: Key Algorithms z Connectivity z Sorting earching z Searching z Hashing z Key data structures
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Part II: Statistical Methods z Random Numbers z Markov-Chain Monte-Carlo Metropolis-Hastings Gibbs Sampling unction Optimization z Function Optimization Naïve algorithms Newton’s Methods e to s et ods E-M algorithm z Numerical Integration
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Textbooks z Algorithms in C Sedgewick (1998) 3 rd edition printed in 1998 z Numerical Recipes in C Press, Teukolsky, Vetterling, Flannery 2 nd edition printed in 2002
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Assessment for 615 z Weekly Assignments About 50% of the final mark z 2 Exams bout 50% of the final mark About 50% of the final mark
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Assessment for 815 z Weekly Assignments About 33% of the final mark z 2 Exams About 33% of the final mark z Project, to be completed in pairs About 33% of the final mark
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Office Hours z Please fill out doodle poll with your availability: www.doodle.ch/tsuhaiaqe4ar5cer z My office: School of Public Health II, Crossroads Level 4 z My e-mail: goncalo@umich.edu
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Algorithms z Methods for solving problems that are well suited to computer implementation z Good algorithms make apparently possible problems become simple impossible problems become simple
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Algorithms are ideas … z Focus on approach to a problem ypically the actual implementation z Typically, the actual implementation could be take many different forms omputer languages Computer languages Pen and paper
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xample: Example: DNA Sequence Matches z When the Human Genome Project started, searching through the entire enome sequence seemed impractical genome sequence seemed impractical… z For example, Searching for ~150 sequences of about 00bp each in ~3 000 000 000 bases of 500bp each in 3,000,000,000 bases of sequence would take ~3 hours with the original BLAST or FASTA3 algorithms
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xample: Example: DNA Sequence Matches z Mullikin and colleagues (2001) described an improved algorithm, using hash tables, that could do this in < 2 seconds z Reference: ing, Cox and Mullikin (2001) enome Ning, Cox and Mullikin (2001) Genome Research 11: 1725-1729
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615.01 - Biostatistics 615/815 Statistical Computing Gonalo...

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