This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampling, it reviews sequentialized and customized designs. It ends with topics for future research
Abstract: This article surveys optimization of simulated systems. The simulation may be either deter...
This survey considers the optimization of simulated systems. The simulation may be either determinis...
This paper proposes a novel method to select an experimental design for interpolation in random simu...
Kriging provides metamodels for deterministic and random simulation models. Actually, there are seve...
Many scientific disciplines use mathematical models to describe complicated real systems. Often, ana...
This article reviews the design and analysis of simulation experiments. It focusses on analysis via ...
Kriging provides metamodels for deterministic and random simulation models. Actually, there are seve...
Kriging (Gaussian process, spatial correlation) metamodels approximate the Input/Output (I/O) functi...
Kriging metamodels (also called Gaussian process or spatial correlation models) approximate the Inpu...
This chapter surveys two methods for the optimization of real-world systems that are modelled throug...
Kriging metamodels (also called Gaussian process or spatial correlation models) approximate the Inpu...
This paper explores the effects of the correlation model, the trend model, and the number of trainin...
This article surveys optimization of simulated systems. The simulation may be either deterministic o...
Kriging (or Gaussian Process) metamodels may be analyzed through bootstrapping, which is a versatile...
We extend the basic theory of kriging, as applied to the design and analysis of deterministic comput...
Abstract: This article surveys optimization of simulated systems. The simulation may be either deter...
This survey considers the optimization of simulated systems. The simulation may be either determinis...
This paper proposes a novel method to select an experimental design for interpolation in random simu...
Kriging provides metamodels for deterministic and random simulation models. Actually, there are seve...
Many scientific disciplines use mathematical models to describe complicated real systems. Often, ana...
This article reviews the design and analysis of simulation experiments. It focusses on analysis via ...
Kriging provides metamodels for deterministic and random simulation models. Actually, there are seve...
Kriging (Gaussian process, spatial correlation) metamodels approximate the Input/Output (I/O) functi...
Kriging metamodels (also called Gaussian process or spatial correlation models) approximate the Inpu...
This chapter surveys two methods for the optimization of real-world systems that are modelled throug...
Kriging metamodels (also called Gaussian process or spatial correlation models) approximate the Inpu...
This paper explores the effects of the correlation model, the trend model, and the number of trainin...
This article surveys optimization of simulated systems. The simulation may be either deterministic o...
Kriging (or Gaussian Process) metamodels may be analyzed through bootstrapping, which is a versatile...
We extend the basic theory of kriging, as applied to the design and analysis of deterministic comput...
Abstract: This article surveys optimization of simulated systems. The simulation may be either deter...
This survey considers the optimization of simulated systems. The simulation may be either determinis...
This paper proposes a novel method to select an experimental design for interpolation in random simu...