Conformal prediction beyond exchangeability

salesforce/online_conformal 27 Feb 2022

Our new methods are provably robust, with substantially less loss of coverage when exchangeability is violated due to distribution drift or other challenging features of real data, while also achieving the same coverage guarantees as existing conformal prediction methods if the data points are in fact exchangeable.


0.03 stars / hour

Bambi: A simple interface for fitting Bayesian linear models in Python

bambinos/bambi 19 Dec 2020

The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications.


0.02 stars / hour

Tutorial: Effective visual communication for the quantitative scientist

GraphicsPrinciples/ 22 Mar 2019

With this competency, we can better understand data and influence decisions towards appropriate actions.


0.02 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

Beating the bookies with their own numbers - and how the online sports betting market is rigged

Lisandro79/BeatTheBookie 8 Oct 2017

The online sports gambling industry employs teams of data analysts to build forecast models that turn the odds at sports games in their favour.

Applications Other Computer Science Other Statistics

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.


0.02 stars / hour

Bayesian Workflow

zhaoolee/garss 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.


0.02 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

Estimating Treatment Effects with Causal Forests: An Application

grf-labs/grf 20 Feb 2019

We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges.


0.02 stars / hour

StateSpaceModels.jl: a Julia Package for Time-Series Analysis in a State-Space Framework

LAMPSPUC/StateSpaceModels.jl 5 Aug 2019

StateSpaceModels. jl is an open-source Julia package for modeling, forecasting and simulating time series in a state-space framework.

Computation Optimization and Control

0.01 stars / hour