标题： 基于顺序线性模拟推理的宇宙参数估计 显示英文标题

标题： Cosmological Parameter Estimation with Sequential Linear Simulation-based Inference

摘要： 我们开发了线性仿真推断（Linear Simulation-based Inference, LSBI）的框架，这是一种仿真推断的应用，其中似然函数由其参数的高斯线性函数近似。 我们获得了基于从模拟器抽取样本的超参数后验分布的解析表达式，适用于均匀先验和共轭先验。 该方法依次应用于几个toy模型，并在宇宙微波背景温度功率谱的模拟数据集上进行了测试。 我们发现经过四到五轮$\mathcal{O}(10^4)$模拟后收敛，这与最先进的神经密度估计方法具有竞争力。 因此，我们证明了在保持可解释性和智力监督的同时，可以获得显著的信息增益并生成与潜在参数一致的后验分布。 显示英文摘要

摘要： We develop the framework of Linear Simulation-based Inference (LSBI), an application of simulation-based inference where the likelihood is approximated by a Gaussian linear function of its parameters. We obtain analytical expressions for the posterior distributions of hyper-parameters of the linear likelihood in terms of samples drawn from a simulator, for both uniform and conjugate priors. This method is applied sequentially to several toy-models and tested on emulated datasets for the Cosmic Microwave Background temperature power spectrum. We find that convergence is achieved after four or five rounds of $\mathcal{O}(10^4)$ simulations, which is competitive with state-of-the-art neural density estimation methods. Therefore, we demonstrate that it is possible to obtain significant information gain and generate posteriors that agree with the underlying parameters while maintaining explainability and intellectual oversight.