#### Filter Results:

#### Publication Year

1994

2015

#### Publication Type

#### Co-author

#### Publication Venue

#### Key Phrases

#### Method

#### Organism

Learn More

A smooth response surface (SRS) algorithm is developed as an elaborate data mining technique for analyzing gene expression data and constructing a gene regulatory network. A three-dimensional SRS is generated to capture the biological relationship between the target and activator-repressor. This new technique is applied to functionally describe triplets of… (More)

Absfract-We propose, implement, and evaluate a class of nonstationary-state hidden Markov models (HMM's) having each state associated with a distinct polynomial regression function of time plus white Gaussian noise. The model represents the transitional acoustic trajectories of speech in a parametric manner, and includes the standard stationary-state HMM as… (More)

Standard practice in analyzing data from different types of experiments is to treat data from each type separately. By borrowing strength across multiple sources, an integrated analysis can produce better results. Careful adjustments need to be made to incorporate the systematic differences among various experiments. To this end, some Bayesian hierarchical… (More)

Modeling experiments with qualitative and quantitative factors is an important issue in computer modeling. A framework for building Gaussian process models that incorporate both types of factors is proposed. The key to the development of these new models is an approach for constructing correlation functions with qualitative and quantitative factors. An… (More)

Preliminary design of a complex system often involves exploring a broad design space. This may require repeated use of computationally expensive simulations. To ease the computational burden, surrogate models are built to provide rapid approximations of more expensive models. However, the surrogate models themselves are often expensive to build because they… (More)

Categorical data arise quite often in industrial experiments because of an expensive or inadequate measurement system for obtaining continuous data. When the failure probability/defect rate is small, experiments with categorical data provide little information regarding the effect of factors of interests and are generally not useful for product/process… (More)

1 Bayesian Analysis We present a Bayesian approach to analyze the two proposed nonstationary Gaussian process models with covariance functions (15) and (16). For simplicity, we consider only the model (17) in the paper. Suppose in the computer experiment we have n runs of the simulation code at

Robust parameter design (or, briefly, parameter design) has been widely used as a cost effective tool to reduce process variability by appropriate selection of control factors to make the process insensitive to noise. However, when there are strong noise factors in the process, use of parameter design alone may not be effective and an on-line control… (More)