标题： 基于仿真的预测和推断的Fisher评分匹配 显示英文标题

标题： Fisher Score Matching for Simulation-Based Forecasting and Inference

摘要： 我们提出一种方法，通过分数匹配来估计Fisher评分——即相对于模型参数的对数似然梯度。 通过引入一个潜在参数模型，我们表明可以通过训练神经网络通过均方误差损失来预测潜在评分，从而学习Fisher评分。 我们在一个简单的线性高斯模型和一个宇宙学示例中验证了我们的方法，使用了一个可微分模拟器。 在两种情况下，对于合理的数据-参数对，学习到的评分与真实值非常接近。 该方法扩展了在模拟模型中执行Fisher预测和基于梯度的贝叶斯推断的能力，即使它们不可微；因此，它在推进宇宙学分析方面具有广泛潜力。 显示英文摘要

摘要： We propose a method for estimating the Fisher score--the gradient of the log-likelihood with respect to model parameters--using score matching. By introducing a latent parameter model, we show that the Fisher score can be learned by training a neural network to predict latent scores via a mean squared error loss. We validate our approach on a toy linear Gaussian model and a cosmological example using a differentiable simulator. In both cases, the learned scores closely match ground truth for plausible data-parameter pairs. This method extends the ability to perform Fisher forecasts, and gradient-based Bayesian inference to simulation models, even when they are not differentiable; it therefore has broad potential for advancing cosmological analyses.