In this work, we propose a novel approximated collapsed variational Bayes approach to model selection in linear regression. The approximated collapsed variational Bayes algorithm offers improvement…
We show that the global minimum solution of ||A - BXC|| can be found in closed form with singular value decompositions and generalized singular value decompositions for a variety of constraints on …
An important problem in single-player video game design is how to sequence game elements within a level (or “chunk”) of the game. Each element has two critical features: a reward (e.g., earning…
Corporate probability of default (PD) prediction is vitally important for risk management and asset pricing. In search of accurate PD prediction, we propose a flexible yet easy-to-interpret default…
Model averaging has a rich history dating from its use for combining forecasts from time-series models (Bates and Granger) and presents a compelling alternative to model selection methods. We propo…
Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved significantly by …
We demonstrate a short-time long distance distributed high-temperature sensing by non-local Haar transform (NLH) in optical frequency domain reflectometry (OFDR). By searching similar pixels across…
Pipeline integrity management is of great significance for ensuring the safety of life and property. Due to the stable running, no pressure drops, and hidden characteristics, the identification and…
Life tests for highly reliable products often take a long time even using accelerated life testing with censoring. When the production process is monitored by control charts with the product lifeti…
The Periodic Gaussian Process (PGP) has been increasingly used to model periodic data due to its high accuracy. Yet, computing the likelihood of PGP has a high computational complexity of O(n3) (n …