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

0.12 stars / hour

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.


0.05 stars / hour

Nested $\hat R$: Assessing Convergence for Markov chain Monte Carlo when using many short chains

google-research/google-research 25 Oct 2021

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.


0.03 stars / hour

Synthetic Difference in Differences

synth-inference/synthdid 24 Dec 2018

We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods.


0.03 stars / hour

ruptures: change point detection in Python

deepcharles/ruptures 2 Jan 2018

ruptures is a Python library for offline change point detection.

Computation Mathematical Software

0.02 stars / hour

A flexible forecasting model for production systems

linkedin/greykite 3 May 2021

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


0.02 stars / hour

Probabilistic Programming in Python using PyMC

pymc-devs/pymc3 29 Jul 2015

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


0.01 stars / hour

Markov-Switching State-Space Models with Applications to Neuroimaging

ddegras/switch-ssm 9 Jun 2021

State-space models (SSM) with Markov switching offer a powerful framework for detecting multiple regimes in time series, analyzing mutual dependence and dynamics within regimes, and asserting transitions between regimes.

Methodology Applications

0.01 stars / hour

Non-parametric Quantile Regression via the K-NN Fused Lasso

stevenysw/qt_knnfl 3 Dec 2020

Quantile regression is a statistical method for estimating conditional quantiles of a response variable.

Methodology Statistics Theory Statistics Theory

0.01 stars / hour

Bayesian Workflow

storopoli/Bayesian-Julia 3 Nov 2020

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


0.01 stars / hour