# Orbit: Probabilistic Forecast with Exponential Smoothing

18 Apr 2020

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

Computation Methodology

809
0.12 stars / hour

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

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.

Applications

4,183
0.05 stars / hour

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

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.

Methodology

20,628
0.03 stars / hour

# Synthetic Difference in Differences

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.

Methodology

125
0.03 stars / hour

# ruptures: change point detection in Python

2 Jan 2018

ruptures is a Python library for offline change point detection.

Computation Mathematical Software

858
0.02 stars / hour

# A flexible forecasting model for production systems

3 May 2021

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

Methodology

1,351
0.02 stars / hour

# Probabilistic Programming in Python using PyMC

29 Jul 2015

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

Computation

6,189
0.01 stars / hour

# Markov-Switching State-Space Models with Applications to Neuroimaging

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

7
0.01 stars / hour

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

3 Dec 2020

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

Methodology Statistics Theory Statistics Theory

4
0.01 stars / hour

# Bayesian Workflow

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.

Methodology

68
0.01 stars / hour