Wang Xiao-Feng
Department of Quantitative Health Sciences/Biostatistics Section, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA.
J Biom Biostat. 2013 Jun 25;4. doi: 10.4172/2155-6180.1000e125.
Integrated nested Laplace approximations (INLA) are a recently proposed approximate Bayesian approach to fit structured additive regression models with latent Gaussian field. INLA method, as an alternative to Markov chain Monte Carlo techniques, provides accurate approximations to estimate posterior marginals and avoid time-consuming sampling. We show here that two classical nonparametric smoothing problems, nonparametric regression and density estimation, can be achieved using INLA. Simulated examples and R functions are demonstrated to illustrate the use of the methods. Some discussions on potential applications of INLA are made in the paper.
集成嵌套拉普拉斯近似法（INLA）是最近提出的一种近似贝叶斯方法，用于拟合具有潜在高斯场的结构化加法回归模型。作为马尔可夫链蒙特卡罗技术的替代方法，INLA方法能提供准确的近似值以估计后验边缘分布，并避免耗时的抽样。我们在此表明，使用INLA可以实现两个经典的非参数平滑问题，即非参数回归和密度估计。文中给出了模拟示例和R函数来说明这些方法的使用。本文还对INLA的潜在应用进行了一些讨论。
