Trending Research
$R^*$: A robust MCMC convergence diagnostic with uncertainty using gradient-boosted machines
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
nflWAR: A Reproducible Method for Offensive Player Evaluation in Football
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
Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference
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: change point detection in Python
ruptures is a Python library for offline change point detection.
Computation Mathematical Software
A parsimonious family of multivariate Poisson-lognormal distributions for clustering multivariate count data
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: an R Package for Univariate Graph-Constrained Change-Point Detection
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
A large-scale analysis of racial disparities in police stops across the United States
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
Bayesian Workflow
zhaoolee/garss
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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
Bambi: A simple interface for fitting Bayesian linear models in Python
The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications.
Computation
Bayesian Uncertainty Quantification for Systems Biology Models Parameterized Using Qualitative Data
Motivation: Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models.
Methodology Quantitative Methods
