AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models
AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models
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DOI：
10.1080/10556788.2011.597854
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发表时间：
2012-01-01
影响因子：
2.2
通讯作者：
Sibert, John
中科院分区：
文献类型：
--
作者：
Fournier, David A.;Skaug, Hans J.;Sibert, John
统计参数估计的许多准则，如最大似然，被制定为一个非线性优化问题。自动微分模型生成器（ADMB）是一个基于自动微分的编程框架，针对具有大量参数的高度非线性模型。使用AD的好处是计算效率和高数值精度，这两个在许多实际问题中至关重要。我们描述了ADMB的基本组件和基本原理，重点介绍了其他统计软件中没有的功能。这种特征的一个示例是用于潜变量模型的高维积分的拉普拉斯近似的通用实现。我们还回顾了文献中，ADMB已被使用，并讨论了ADMB作为一个开源项目的未来发展。总体而言，ADMB的主要优势是灵活性、速度、精度、稳定性和量化不确定性的内置方法。
Many criteria for statistical parameter estimation, such as maximum likelihood, are formulated as a nonlinear optimization problem. Automatic Differentiation Model Builder (ADMB) is a programming framework based on automatic differentiation, aimed at highly nonlinear models with a large number of parameters. The benefits of using AD are computational efficiency and high numerical accuracy, both crucial in many practical problems. We describe the basic components and the underlying philosophy of ADMB, with an emphasis on functionality found in no other statistical software. One example of such a feature is the generic implementation of Laplace approximation of high-dimensional integrals for use in latent variable models. We also review the literature in which ADMB has been used, and discuss future development of ADMB as an open source project. Overall, the main advantages of ADMB are flexibility, speed, precision, stability and built-in methods to quantify uncertainty.