Recent studies have shown that novel continuous dropout methods can be viewed as a Bayesian interpretation of model parameters, though most such studies have shown results using normal distributions. As the posterior distributions over neural network nodes and parameters are intractable, given that they are a result of artificial construction...
New capabilities in wireless network security are now possible through deep learning, which can identify and leverage patterns in radio frequency (RF) data. One area of deep learning, known as open set detection, is focused on detecting data instances from new devices encountered during deployment that were not previously seen...
Diverse scientific fields collect multiple time series data to investigate the dynamical behavior of complex systems: atmospheric and climate science, geophysics, neuroscience, epidemiology, ecology, and environmental science. Identifying patterns of mutual dependence among such data generates valuable knowledge that can be applied either for inferential or forecasting purposes. Vector autoregressive...
Spatial-temporal data arises in many applications, for example, environment sciences and disease mapping. This dissertation focuses on Gaussian spatial-temporal data. To make statistical inference for Gaussian spatial-temporal data, we developed a special class of spatial-temporal Gaussian state-space models in which the state vectors are constructed following spatial-temporal Gaussian autoregressions that...
The developmental milestones of Sustainability are consistent with the post-normal versus traditional science, where transdisciplinary and policy/action research are among the important approaches to be added to traditional analysis. This shift requires a new perspective to look at the problem at hand: we are no longer considering a group of...
Density dependence is an ecological concept concerning the mechanisms of change in the size of a population. The inability to census ecological populations confounds approaches to identify and quantify the level of density dependence. Statistical tests which ignore the presence of measurement error tend to result in misspeci fied type...
The fixed bed drying of western hemlock and Douglas-fir biomass particles at temperatures ranging from 50°C to 200°C and air velocities from 0.3 to 0.9 m/s was investigated. The objectives were to describe the drying characteristics of the particles, fit a model for thin-layer drying, and develop and test a...
A copula is the representation of a multivariate distribution. Copulas are used to model multivariate data in many fields. Recent developments include copula models for spatial data and for discrete marginals. We will present a new methodological approach for modeling discrete spatial processes and for predicting the process at unobserved...
The author of the Highway Safety Manual (HSM) Part B developed a predictive method for safety management. A software tool for highway safety system analysis called the SafetyAnalyst is developed basing on HSM Part B. The author describes an effort to evaluate the feasibility of SafetyAnalyst in Oregon. Seven sample...
The first edition of the Highway Safety Manual (HSM) provides a quantitative
approach to predict the safety of transportation facilities based on the recently
developed scientific methods. This approach, known as the predictive method, was
developed for several states in the United States. Due to differences in driver
population, weather...