用隐式分位数网络替代高斯过程，实现更高效、灵活的模拟计算。
Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
- 基于隐式分位数网络学习全条件分位函数，突破传统高斯过程局限。
- 在14个基准测试中，分位数损失降低11%~26%，可处理9万样本数据。
- 适合复杂跳跃函数和主动学习场景，尤其在非平稳数据上表现优异。
高斯过程（GP）是模拟昂贵计算机实验的主流工具，但其立方级计算成本、平稳性假设及高斯预测分布限制了应用范围。本文提出生成式贝叶斯计算（GBC），通过隐式分位数网络（IQNs）构建替代框架，解决上述三重局限。GBC从输入-输出对中学习完整的条件分位数函数；测试时，每个分位数水平仅需一次前向传播即可生成预测分布样本。在14个基准测试中，对比四种基于GP的方法，GBC在分段跳跃过程任务中将连续分位数评分（CRPS）降低11%–26%，在十维Friedman函数上降低14%，并能线性扩展至90,000个训练点，而稠密协方差GP在此已不可行。边界增强型变体在二维跳跃数据集上与模块化跳跃GP相当或更优，最高提升46%的CRPS。在主动学习中，随机先验的IQN集成模型在Rocket LGBB任务上相比深度高斯过程主动学习，均方根误差（RMSE）接近降低三倍。总体而言，GBC在14次比较中有12次取得有利点估计。对于光滑曲面，高斯过程仍因平滑先验提供有效正则化而保持优势。
原文摘要 · Abstract (English)
Gaussian process (GP) surrogates are the default tool for emulating expensive computer experiments, but cubic cost, stationarity assumptions, and Gaussian predictive distributions limit their reach. We propose Generative Bayesian Computation (GBC) via Implicit Quantile Networks (IQNs) as a surrogate framework that targets all three limitations. GBC learns the full conditional quantile function from input--output pairs; at test time, a single forward pass per quantile level produces draws from the predictive distribution. Across fourteen benchmarks we compare GBC to four GP-based methods. GBC improves CRPS by 11--26\% on piecewise jump-process benchmarks, by 14\% on a ten-dimensional Friedman function, and scales linearly to 90,000 training points where dense-covariance GPs are infeasible. A boundary-augmented variant matches or outperforms Modular Jump GPs on two-dimensional jump datasets (up to 46\% CRPS improvement). In active learning, a randomized-prior IQN ensemble achieves nearly three times lower RMSE than deep GP active learning on Rocket LGBB. Overall, GBC records a favorable point estimate in 12 of 14 comparisons. GPs retain an edge on smooth surfaces where their smoothness prior provides effective regularization.
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