mlsanityData to 14 Oct 2026

Streaming Bayesian Modeling for predicting Fat-Tailed Customer Lifetime Value

arXiv 2312.00373 ↗First posted 1 Dec 2023cs.LG stat.AP stat.MEcited by 0

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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.

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.

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12 of 30 references.

References · 12

  1. 2013Auto-Encoding Variational Bayescited by 4,053
  2. 2020Learning under Concept Drift: A Reviewcited by 477
  3. 2014Stochastic Gradient Hamiltonian Monte Carlocited by 343
  4. 2021Unsolved Problems in ML Safetycited by 292
  5. 2018Online Learning: A Comprehensive Surveycited by 205
  6. 2020River: machine learning for streaming data in Pythoncited by 98
  7. 2017Sparsity information and regularization in the horseshoe and other shrinkage priorscited by 43
  8. 2015Automatic Variational Inference in Stancited by 24
  9. 2020Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applicationscited by 16
  10. 2020URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networkscited by 9
  11. 2020On Single Point Forecasts for Fat-Tailed Variablescited by 3
  12. 2023Adaptive Sparse Gaussian Processcited by 3

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