Markov chain Monte Carlo (MCMC) has transformed Bayesian model inference over the past three decades and is now a workhorse of applied scientists.

Methodology Applications

We discuss how our reproducible WAR framework, built entirely on publicly available data, can be easily extended to estimate WAR for players at any position, provided that researchers have access to data specifying which players are on the field during each play.

Applications 62P99

I prove that MAVB provides a guaranteed improvement in the approximation quality at low computational cost and induces dependencies that were assumed away by the initial factorization assumptions.

Methodology

ruptures is a Python library for offline change point detection.

Computation Mathematical Software

Due to this hierarchical structure, the MPLN model can account for over-dispersion as opposed to the traditional Poisson distribution and allows for correlation between the variables.

Computation 62H30

gfpop works for a user-defined graph that can encode prior assumptions about the types of change that are possible and implements several loss functions (Gauss, Poisson, binomial, biweight and Huber).

Computation 62M10, 60J22

We find that black drivers are stopped more often than white drivers relative to their share of the driving-age population, but that Hispanic drivers are stopped less often than whites.

Applications

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory.

Methodology

The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications.

Computation

Motivation: Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models.

Methodology Quantitative Methods