Description: Survey of core algorithms for statistical computing in biostatistics. Topics include divide-and-conquer algorithms, random number generation, numerical integration, optimization, Monte Carlo methods, and the EM algorithm. Students learn to interpret computational results and implement statistical methods in R and Python, leveraging generative AI tools.
Prerequisites: Biostat 601, Biostat 602, Biostat 666 or Perm. Instr.
Description: Advanced topics in quantitative genetics with emphasis on models for gene mapping, pedigree analysis, reconstruction of evolutionary trees, and molecular genetics experiments, computational mathematics, and statistical techniques such as Chen-Stein Poisson approximations, hidden Markov chains, and the EM algorithm introduced as needed.
