Abstract This article proposes a general optimization strategy, which combines results from different optimization or parameter estimation methods to overcome shortcomings of a single method. Shotgun optimization is developed as a framework which employs different optimization strategies, criteria, or conditional targets to enable wider likelihood exploration. The introduced shotgun optimization approach is embedded into an incremental mixture importance sampling algorithm to produce improved posterior samples for multimodal densities and creates robustness in cases where the likelihood and prior are in disagreement. Despite using different optimization approaches, the samples are combined into samples from a single target posterior. The diversity of the framework is demonstrated on parameter estimation from differential equation models employing diverse strategies including numerical solutions and approximations thereof. Additionally the approach is demonstrated on mixtures of discrete and continuous parameters and is shown to ease estimation from synthetic likelihood models. R code of the implemented examples can be found at https://github.com/BiljanaJSJ/IMIS-ShOpt. Supplementary materials for this article are available online.

中文翻译：

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使用 Shotgun 优化进行增量混合重要性采样

摘要 本文提出了一种通用的优化策略，它结合了不同优化或参数估计方法的结果，以克服单一方法的缺点。Shotgun 优化被开发为一个框架，该框架采用不同的优化策略、标准或条件目标来实现更广泛的可能性探索。引入的霰弹枪优化方法嵌入到增量混合重要性采样算法中，为多模态密度生成改进的后验样本，并在似然和先验不一致的情况下创建鲁棒性。尽管使用了不同的优化方法，但样本被组合成来自单个目标后验的样本。该框架的多样性在从采用不同策略（包括数值解及其近似）的微分方程模型的参数估计中得到证明。此外，该方法在离散和连续参数的混合上进行了演示，并显示出可以简化合成似然模型的估计。实现示例的 R 代码可以在 https://github.com/BiljanaJSJ/IMS-ShOpt 找到。本文的补充材料可在线获取。