Bayesian Nonparametric Regression and Density Estimation Using Integrated Nested Laplace Approximations

Journal of Biometrics & Biostatistics

article

Xiao-Feng Wang¹ 

[1] Department of Quantitative Health Sciences/Biostatistics Section, Cleveland Clinic Lerner Research Institute

关键词: Nonparametric regression;    Density estimation;    Approximate Bayesian inference;    Integrated nested Laplaceapproximations;    Markov chain Monte Carlo;   

DOI  :  10.4172/2155-6180.1000e125

来源: Hilaris Publisher

【 摘 要 】

Integrated nested Laplace approximations (INLA) are a recently proposed approximate Bayesian approach tofit structured additive regression models with latent Gaussian field. INLA method, as an alternative to Markov chainMonte Carlo techniques, provides accurate approximations to estimate posterior marginals and avoid time-consumingsampling. We show here that two classical nonparametric smoothing problems, nonparametric regression and densityestimation, can be achieved using INLA. Simulated examples and R functions are demonstrated to illustrate the use ofthe methods. Some discussions on potential applications of INLA are made in the paper.

【 授权许可】

Unknown