mlsanityData to 7 Oct 2026

Generative Bayesian Computation for Maximum Expected Utility

arXiv 2408.16101 ↗First posted 28 Aug 2024stat.CO stat.MLcited by 3

Not on the map. Why?

Abstract (arXiv)

Generative Bayesian Computation (GBC) methods are developed to provide an efficient computational solution for maximum expected utility (MEU). We propose a density-free generative method based on quantiles that naturally calculates expected utility as a marginal of quantiles. Our approach uses a deep quantile neural estimator to directly estimate distributional utilities. Generative methods assume only the ability to simulate from the model and parameters and as such are likelihood-free. A large training dataset is generated from parameters and output together with a base distribution. Our method a number of computational advantages primarily being density-free with an efficient estimator of expected utility. A link with the dual theory of expected utility and risk taking is also discussed. To illustrate our methodology, we solve an optimal portfolio allocation problem with Bayesian learning and a power utility (a.k.a. fractional Kelly criterion). Finally, we conclude with directions for future research.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 49 references that are in the map. First posted on arXiv 28 Aug 2024.

Builds on

18 of 49 references.

References · 18

  1. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 464
  2. 2017Nonparametric regression using deep neural networks with ReLU activation functioncited by 431
  3. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 362
  4. 2018Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flowscited by 217
  5. 2018Does data interpolation contradict statistical optimality?cited by 95
  6. 2015Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Modelscited by 61
  7. 2016Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimationcited by 50
  8. 2018Posterior Concentration for Sparse Deep Learningcited by 50
  9. 2023High-dimensional analysis of double descent for linear regression with random projectionscited by 47
  10. 2015Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Networkcited by 39
  11. 2024Neural Methods for Amortized Inferencecited by 36
  12. 2022Likelihood-Free Parameter Estimation with Neural Bayes Estimatorscited by 31
6 more
  1. 2020Quantile regression with deep ReLU Networks: Estimators and minimax ratescited by 24
  2. 2017Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Tradingcited by 11
  3. 2022Learning Summary Statistics for Bayesian Inference with Autoencoderscited by 9
  4. 2022Generative Bayesian Inference with GANscited by 5
  5. 2023Generative Causal Inferencecited by 4
  6. 2021Approximate Bayesian Computation via Classificationcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Solving Empirical Bayes via Transformerscited by 7
  2. 2025Generative Learner for Distributional Causal Effectscited by 0
  3. 2025Generative Bayesian Hyperparameter Tuningcited by 0