Capitalising on Opportunistic Data for Monitoring Species Relative Abundances

howdyai/botkit 26 Feb 2015

Opportunistic data combined with a relatively small amount of data collected with a known effort may thus provide access to accurate and precise estimates of quantitative changes in relative abundance over space and/or time.


Multilevel Delayed Acceptance MCMC

pymc-devs/pymc 8 Feb 2022

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

Probabilistic Programming in Python using PyMC

pymc-devs/pymc3 29 Jul 2015

Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code.


A large-scale analysis of racial disparities in police stops across the United States

rfordatascience/tidytuesday 18 Jun 2017

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.


HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data

ContextLab/hypertools 28 Jan 2017

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

tdunning/t-digest 11 Feb 2019

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

linkedin/greykite 3 May 2021

This paper discusses desirable properties of forecasting models in production systems.


Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling

uber/orbit 7 Jun 2021

Both Bayesian and varying coefficient models are very useful tools in practice as they can be used to model parameter heterogeneity in a generalizable way.

Applications Methodology

Orbit: Probabilistic Forecast with Exponential Smoothing

uber/orbit 18 Apr 2020

Time series forecasting is an active research topic in academia as well as industry.

Computation Methodology

ruptures: change point detection in Python

deepcharles/ruptures 2 Jan 2018

ruptures is a Python library for offline change point detection.

Computation Mathematical Software