Top Papers
Nested $\hat R$: Assessing Convergence for Markov chain Monte Carlo when using many short chains
google-research/google-research
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Furthermore we use $\mathfrak n \hat R$ to construct a warmup scheme with an adaptive length, allowing users to avoid running their MCMC algorithms longer than necessary.
Methodology
Probabilistic Programming in Python using PyMC
pymc-devs/pymc3
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Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code.
Computation
Multilevel Delayed Acceptance MCMC
pymc-devs/pymc
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We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target distribution.
Methodology Computation 62F15, 62M05, 65C05, 65C40
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
HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data
Just as the position of an object moving through space can be visualized as a 3D trajectory, HyperTools uses dimensionality reduction algorithms to create similar 2D and 3D trajectories for time series of high-dimensional observations.
Other Statistics
Computing Extremely Accurate Quantiles Using t-Digests
We present on-line algorithms for computing approximations of rank-based statistics that give high accuracy, particularly near the tails of a distribution, with very small sketches.
Sequential Quantile Estimation
Computation
Data Structures and Algorithms
A flexible forecasting model for production systems
This paper discusses desirable properties of forecasting models in production systems.
Methodology
Orbit: Probabilistic Forecast with Exponential Smoothing
Time series forecasting is an active research topic in academia as well as industry.
Computation Methodology
ruptures: change point detection in Python
ruptures is a Python library for offline change point detection.
Computation Mathematical Software
Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
Our model estimates these changes by calculating backwards from temporal data on observed to estimate the number of infections and rate of transmission that occurred several weeks prior, allowing for a probabilistic time lag between infection and death.
Applications Methodology
