Statistical Inference for Data Scientists Mathematical and computational approaches to estimation and inference from frequentist and Bayesian perspectives. Sampling distributions; maximum likelihood estimation; computational maximization of likelihoods, including grid search, Newton- Raphson methods; likelihood ratio tests. Simulations of power and error rates. Introduction to Bayesian inference; prior and posterior distributions; model building; sampling from the posterior distribution; MCMC algorithms. Prerequisites: CSCI 3360 and STAT 4510/6510 Semester Offered: Spring Level: Undergraduate
