A Deep Probabilistic Model for Customer Lifetime Value Prediction

google/lifetime-value 16 Dec 2019

In this article, we model the distribution of LTV given associated features as a mixture of zero point mass and lognormal distribution, which we refer to as the zero-inflated lognormal (ZILN) distribution.


0.01 stars / hour

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

0.01 stars / hour

nflWAR: A Reproducible Method for Offensive Player Evaluation in Football

mrcaseb/nflfastr 3 Feb 2018

We discuss how our reproducible WAR framework, built entirely on publicly available data, can be easily extended to estimate WAR for players at any position, provided that researchers have access to data specifying which players are on the field during each play.

Applications 62P99

0.01 stars / hour

Applied Measure Theory for Probabilistic Modeling

cscherrer/MeasureTheory.jl 1 Oct 2021

Probabilistic programming and statistical computing are vibrant areas in the development of the Julia programming language, but the underlying infrastructure dramatically predates recent developments.

Computation Programming Languages

0.01 stars / hour

Discrepancy measures for sensitivity analysis in mathematical modeling

arnaldpuy/discrepancy 27 Jun 2022

While Sensitivity Analysis (SA) improves the transparency and reliability of mathematical models, its uptake by modelers is still scarce.


0.01 stars / hour

Tutorial: Effective visual communication for the quantitative scientist

GraphicsPrinciples/GraphicsPrinciples.github.io 22 Mar 2019

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


0.01 stars / hour

Synthetic Difference in Differences

dropout009/sdid_python 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.


0.01 stars / hour

Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects

jaredsmurray/bcf 29 Jun 2017

This paper develops a semi-parametric Bayesian regression model for estimating heterogeneous treatment effects from observational data.


0.01 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.


0.01 stars / hour

Basis-Function Models in Spatial Statistics

msainsburydale/ARSIA_BasisFunctionModels_code 8 Feb 2022

Spatial statistics is concerned with the analysis of data that have spatial locations associated with them, and those locations are used to model statistical dependence between the data.


0.01 stars / hour