NETS 7350 — Bayesian and Network Statistics
4 semester hoursGraduateLectureusually offered: springtypical days: MBostonTraditional
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).
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2026 | 1 | 6 | 15 | 40% | 9.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 100% · T 0% · W 0% · Th 0% · F 0%
Common patterns: M (100% of sections) — in patterns, R means Thursday
Professors
Spring
- Nicholas Beauchamp (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (NETS course descriptions) · Student reviews on RateMyHusky · All NETS courses · Plan it at numap.app