Statistical and Data Sciences 390 - Topics in Statistical and Data Sciences: Bayesian Analysis

Topics in statistics and data science. Statistical methods for analyzing data must be chosen appropriately based on the type and structure of the data being analyzed. The particular methods and types of data studied this in this course vary, but topics may include: categorical data analysis, time series analysis, survival analysis, structural equation modeling, survey methodology, Bayesian methods, resampling methods, spatial statistics, missing data methods, advanced linear models, statistical/machine learning, network science, relational databases, web scraping and text mining. This course may be repeated for credit with different topics. Prerequisites: MTH/SDS 290 or MTH/SDS 291 or MTH/SDS 292. : The Bayesian approach to data analysis is gaining popularity for many reasons: (1) Bayesian methods allow you to interpret new data in light of prior information, formally weaving both into a set of updated information; (2) Bayesian results are easier to interpret; and (3) the computational tools required for applying Bayesian techniques are increasingly accessible. This course explores the Bayesian philosophy, the Bayesian approach to statistical analysis, Bayesian computing, as well as the frequentist versus Bayesian debate. Topics include Bayes' Theorem, prior and posterior probability distributions, Bayesian regression, Bayesian hierarchical models, and an introduction to Markov chain Monte Carlo techniques. Prerequisites: SDS 192, SDS 291 and either MTH 112 or MTH 111 and MTH 153. (E)