Simulation based composite likelihood.

IF 1.6 2区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Statistics and Computing Pub Date : 2025-01-01 Epub Date: 2025-02-25 DOI:10.1007/s11222-025-10584-z

Lorenzo Rimella, Chris Jewell, Paul Fearnhead

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Abstract

Inference for high-dimensional hidden Markov models is challenging due to the exponential-in-dimension computational cost of calculating the likelihood. To address this issue, we introduce an innovative composite likelihood approach called "Simulation Based Composite Likelihood" (SimBa-CL). With SimBa-CL, we approximate the likelihood by the product of its marginals, which we estimate using Monte Carlo sampling. In a similar vein to approximate Bayesian computation (ABC), SimBa-CL requires multiple simulations from the model, but, in contrast to ABC, it provides a likelihood approximation that guides the optimization of the parameters. Leveraging automatic differentiation libraries, it is simple to calculate gradients and Hessians to not only speed up optimization but also to build approximate confidence sets. We present extensive empirical results which validate our theory and demonstrate its advantage over SMC, and apply SimBa-CL to real-world Aphtovirus data.

Supplementary information: The online version contains supplementary material available at 10.1007/s11222-025-10584-z.

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基于模拟的复合可能性。

高维隐马尔可夫模型的推理是具有挑战性的，因为计算可能性的计算成本是指数维的。为了解决这个问题，我们引入了一种创新的复合似然方法，称为“基于模拟的复合似然”（SimBa-CL）。使用SimBa-CL，我们通过其边际的乘积来近似似然，我们使用蒙特卡罗采样来估计。与近似贝叶斯计算（ABC）类似，SimBa-CL需要从模型中进行多次模拟，但是，与ABC相反，它提供了指导参数优化的似然近似。利用自动微分库，可以简单地计算梯度和Hessians，不仅可以加快优化速度，还可以构建近似置信集。我们提出了广泛的实证结果，验证了我们的理论，并证明了其优于SMC的优势，并将SimBa-CL应用于现实世界的阿夫托病毒数据。补充信息：在线版本包含补充资料，提供地址为10.1007/s11222-025-10584-z。

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来源期刊

Statistics and Computing 数学-计算机：理论方法

CiteScore

3.20

自引率

4.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences. In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis. Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification. In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences. Statistics and Computing also publishes book review and software review sections.