论文标题
具有分类输出的基于模拟器的模型的詹森 - 香农发散的非参数可能性推断
Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output
论文作者
论文摘要
在机器学习和统计社区中，基于模拟器的统计模型的无似然推理最近引起了人们的兴趣。这些研究领域的主要重点是通过各种类型的蒙特卡洛采样算法或基于深神经网络的替代模型近似模型参数的后验分布。迄今为止，对基于模拟器的模型的常见推断已经被较少关注，尽管它尤其适合具有大数据的应用，在这些应用中，预期隐式渐近近似可能是准确的，并且可以利用计算有效的策略。在这里，我们得出了一组理论结果，以实现使用Jensen-Shannon Divergence的渐近特性，以实现模型参数的估计，假设检验和构建置信区间。渐近近似为更多计算密集型方法提供了一种快速替代方法，并且对于基于模拟器的模型的不同应用可能具有吸引力。 61
Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities. The primary focus of these research fields has been to approximate the posterior distribution of model parameters, either by various types of Monte Carlo sampling algorithms or deep neural network -based surrogate models. Frequentist inference for simulator-based models has been given much less attention to date, despite that it would be particularly amenable to applications with big data where implicit asymptotic approximation of the likelihood is expected to be accurate and can leverage computationally efficient strategies. Here we derive a set of theoretical results to enable estimation, hypothesis testing and construction of confidence intervals for model parameters using asymptotic properties of the Jensen--Shannon divergence. Such asymptotic approximation offers a rapid alternative to more computation-intensive approaches and can be attractive for diverse applications of simulator-based models. 61
