We present a new Bayesian approach to object identification: variants. By object identification we mean the detection of the member (regular variant) of a given statistical popula...
This paper proposes a robust estimation and validation framework for characterizing local structures in a positive multi-variate continuous function approximated by a Gaussian-base...
In this paper we show how model identifiability is an issue for student modeling: observed student performance corresponds to an infinite family of possible model parameter estimat...
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
As Web applications manipulate an increasing amount of XML, there is a growing interest in storing XML data in relational databases. Due to the mismatch between the complexity of ...