NETS7350 (Bayesian and Network Statistics) course reviews and ratings at Northeastern (NEU). Compare professors with RateMyProfessor reviews.
Introduces advanced quantitative methods including maximum likelihood, hierarchical models, sampling, and network modeling. Offers students an opportunity to engage in estimating and developing models from the probabilistic and Bayesian perspective and to pursue their own research project, focusing on methodological challenges. Reviews probability and examines maximum likelihood methods for estimating regression models with continuous and categorical dependent variables. Examines a variety of procedures for sampling from posterior distributions including grid, quadratic, Gibbs, and Metropolis sampling. Applies these methods to hierarchical modeling and other simple probabilistic models and then takes a closer look at the statistical modeling of networks as it has been developed in the social sciences (e.g., exponential random graph models, temporal models such as TERGM and SIENA, spatial network models, and stochastic block models).
Data updated October 2026.