# Streaming Bayesian Modeling for predicting Fat-Tailed Customer Lifetime Value

arXiv: https://arxiv.org/abs/2312.00373 · HTML: https://mlsanity.com/paper/2312.00373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Stochastic Gradient Hamiltonian Monte Carlo](https://mlsanity.com/paper/1402.4102/) (2014)
- [Unsolved Problems in ML Safety](https://mlsanity.com/paper/2109.13916/) (2021)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Sparsity information and regularization in the horseshoe and other shrinkage priors](https://mlsanity.com/paper/1707.01694/) (2017)
- [Automatic Variational Inference in Stan](https://mlsanity.com/paper/1506.03431/) (2015)
- [Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications](https://mlsanity.com/paper/2001.10488/) (2020)
- [URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks](https://mlsanity.com/paper/2007.04466/) (2020)
- [On Single Point Forecasts for Fat-Tailed Variables](https://mlsanity.com/paper/2007.16096/) (2020)
- [Adaptive Sparse Gaussian Process](https://mlsanity.com/paper/2302.10325/) (2023)

## Abstract (arXiv)

We develop an online learning MCMC approach applicable for hierarchical bayesian models and GLMS. We also develop a fat-tailed LTV model that generalizes over several kinds of fat and thin tails. We demonstrate both developments on commercial LTV data from a large mobile app.