Over the last several decades, potato production has increased globally as it has been recognized as an important component to improving food security. However, potato production has been continuously challenged by pests. Current pest management practices rely heavily on chemical pesticides. Unfortunately, the overuse of pesticides can be harmful to...
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...
Analysis of observations on sequential events over time is common in real life. Sequential measurements over time describing the behavior of systems are usually called time series data, which have been collected in a wide range of disciplines. Over the years there have been multiple research areas in studying stochastic...
In this dissertation, I present experimental observations of multiply charged atomic ions (MCAI) generated from interactions of atomic/molecular clusters with moderately intense nanosecond laser pulses. This process is also known as Coulomb explosion (CE) and it has been extensively studied in intense laser fields using ultrashort pulses. Several prevailing theories...
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...
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...
Diffusion processes in networks are common models for many domains, including species colonization, information/idea cascade, disease propagation and fire spreading. In diffusion networks, a diffusion event occurs when a behavior spreads from one node to the other following a probabilistic model, where the behavior could be species, an idea, a...
The methodology to perform bioequivalence studies for prodrug products has not been finalized by FDA leaving the investigators to speculate over the proper approach. This study innovatively utilized the bootstrap methods for testing in vivo bioequivalence on both parent drug and metabolite of four prodrug products: Clopidogrel, Prednisone, Allopurinol, and...
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...