论文标题
贝叶斯与伊拉（Inla）推断的新途径
A new avenue for Bayesian inference with INLA
论文作者
论文摘要
自Rue等人提出的提案以来，综合嵌套拉普拉斯近似（INLA）一直是成功的贝叶斯推理框架。 （2009）。与基于采样的贝叶斯推断（如MCMC方法）相比，计算效率和准确性提高，是其成功的一些贡献者。 R-Inla的INLA方法和实施方面正在进行的研究，确保了与从业者的持续相关性，并提高了INLA的绩效和适用性。大数据的时代和一些最新的研究发展为重新制定经典inla配方的某些方面的机会提供了一个机会，以更快的推断，改进的数值稳定性和可扩展性。对于数据丰富的模型而言，改进特别明显。我们通过各种数据富含数据的模型的示例来证明效率的提高，例如Cox的比例危害模型，项目响应理论模型，包括预测的空间模型以及用于fMRI数据的三维模型。
Integrated Nested Laplace Approximations (INLA) has been a successful approximate Bayesian inference framework since its proposal by Rue et al. (2009). The increased computational efficiency and accuracy when compared with sampling-based methods for Bayesian inference like MCMC methods, are some contributors to its success. Ongoing research in the INLA methodology and implementation thereof in the R package R-INLA, ensures continued relevance for practitioners and improved performance and applicability of INLA. The era of big data and some recent research developments, presents an opportunity to reformulate some aspects of the classic INLA formulation, to achieve even faster inference, improved numerical stability and scalability. The improvement is especially noticeable for data-rich models. We demonstrate the efficiency gains with various examples of data-rich models, like Cox's proportional hazards model, an item-response theory model, a spatial model including prediction, and a 3-dimensional model for fMRI data.
