Recently introduced prior-encoding deep generative models (e.g., PriorVAE, $\pi$VAE, and PriorCVAE) have emerged as powerful tools for scalable Bayesian inference by emulating complex stochastic processes like Gaussian processes (GPs). However, these methods remain largely a proof-of-concept and inaccessible to practitioners. We propose DeepRV, a lightweight, decoder-only approach that accelerates training, and enhances real-world applicability in comparison to current VAE-based prior encoding approaches. Leveraging probabilistic programming frameworks (e.g., NumPyro) for inference, DeepRV achieves significant speedups while also improving the quality of parameter inference, closely matching full MCMC sampling. We showcase its effectiveness in process emulation and spatial analysis of the UK using simulated data, gender-wise cancer mortality rates for individuals under 50, and HIV prevalence in Zimbabwe. To bridge the gap between theory and practice, we provide a user-friendly API, enabling scalable and efficient Bayesian inference.

翻译：最近提出的先验编码深度生成模型（如PriorVAE、$\pi$VAE和PriorCVAE）通过模拟高斯过程（GPs）等复杂随机过程，已成为可扩展贝叶斯推理的强大工具。然而，这些方法在很大程度上仍停留在概念验证阶段，难以被实际工作者所采用。我们提出了DeepRV，一种轻量级的仅解码器方法，与当前基于VAE的先验编码方法相比，它加速了训练过程并增强了实际应用性。DeepRV利用概率编程框架（如NumPyro）进行推理，在显著提升速度的同时，也提高了参数推理的质量，与完整的MCMC采样结果高度吻合。我们通过模拟数据、50岁以下人群的性别别癌症死亡率以及津巴布韦的HIV流行率数据，展示了其在过程模拟和英国空间分析中的有效性。为弥合理论与实践之间的差距，我们提供了用户友好的API，以实现可扩展且高效的贝叶斯推理。