Bayesian Treed Gaussian Process Models with an Application to Computer Modeling
نویسندگان
چکیده
Motivated by a computer experiment for the design of a rocket booster, this paper explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. The methodological developments and statistical computing details which make this approach efficient are described in detail. In addition to providing an analysis of the rocket booster simulator, our approach is demonstrated to be effective in other arenas.
منابع مشابه
On the Bayesian treed multivariate Gaussian process with linear model of coregionalization
The Bayesian treed multivariate Gaussian process (BTMGP) and Bayesian treed Gaussian process (BTGP) provide straightforward mechanisms for emulating non-stationary multivariate computer codes that alleviate computational demands by fitting models locally. Here, we show that the existing BTMGP performs acceptably when the output variables are dependent but unsatisfactory when they are independen...
متن کاملBayesian Treed Multivariate Gaussian Process With Adaptive Design: Application to a Carbon Capture Unit
Computer experiments are widely used in scientific research to study and predict the behavior of complex systems, which often have responses consisting of a set of non-stationary outputs. The computational cost of simulations at high resolution often is expensive and impractical for parametric studies at different input values. In this paper, we develop a Bayesian treed multivariate Gaussian pr...
متن کاملBayesian treed Gaussian process models
This paper explores nonparametric and semiparametric nonstationary modeling methodologies that couple stationary Gaussian processes and (limiting) linear models with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. Mixing between full Gaussian processes and simple linear models can yield a more parsimonious spatial model while significantly red...
متن کاملTreed Gaussian Process Models for Classification
Recognizing the successes of treed Gaussian process (TGP) models as an interpretable and thrifty model for nonstationary regression, we seek to extend the model to classification. By combining Bayesian CART and the latent variable approach to classification via Gaussian processes (GPs), we develop a Bayesian model averaging scheme to traverse the full space of classification TGPs (CTGPs). We il...
متن کاملBayesian Nonstationary Gaussian Process Models for Large Datasets via Treed Process Convolutions
Spatial modeling often relies upon stationary Gaussian processes (GPs), but the assumption that the correlation structure is independent of the spatial location is invalid in many applications. Various nonstationary GP models have been developed to solve this problem, however, many of them become impractical when the sample size is large. To tackle this problem, we develop a process convolution...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005