---
title: "Bayesian Inference of Expensive Simulators: Survey and Comparison of Design Choices"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.37284v1"
permalink: "https://feed.astralor.com/items/itm_bb81d0ace318b898d7c4633c"
published: "2026-10-27T14:09:16.000Z"
score: 55
channel: "tech"
tags: ["推理效率","RAG","Bayesian Inference","Expensive Simulators","Survey","Comparison","Design Choices","Bayesian"]
---

# Bayesian Inference of Expensive Simulators: Survey and Comparison of Design Choices

## 速览

We present a survey of approaches to Bayesian inference with simulator forward models, focusing on settings with prohibitively expensive simulators. In this domain, Bayesian optimization-based methods have emerged as th…

## 关键点

- We present a survey of approaches to Bayesian inference with simulator forward models, focusing on settings with prohibitively expensive simulators.
- In this domain, Bayesian optimization-based methods have emerged as the most promising approach.
- We unify existing methods into a general framework, enabling consistent reasoning about shared algorithmic structure across the fragmented literature.

## 全文

We present a survey of approaches to Bayesian inference with simulator forward models, focusing on settings with prohibitively expensive simulators. In this domain, Bayesian optimization-based methods have emerged as the most promising approach. We unify existing methods into a general framework, enabling consistent reasoning about shared algorithmic structure across the fragmented literature. Within this framework, we identify the primary design choices and perform a systematic empirical comparison of their effects on inference efficiency in terms of required simulation budget. A key result of our study is that inference efficiency is often dominated by the choice of surrogate-model response variable, as it directly governs the complexity of the modeled function. Specifically, we demonstrate that the prevalent scalar log-likelihood is a poor response variable, as it discards crucial information. Based on this insight, we introduce the concept of custom \emph{proxy variables}: transformations of simulator outputs engineered to retain more information and provide smoother response surfaces, substantially improving inference efficiency over standard response variables. We provide actionable guidance and a numerically robust Julia package to facilitate practical adoption of Bayesian optimization for simulator inverse problems.

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