This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test o...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
A fundamental question in causal inference is whether it is possible to reliably infer the manipulation effects from observational data. There are a variety of senses of asymptot...
In this paper, we propose a content based video categorizing method focusing broadcasted sports videos using camera motion parameters. We define two new features in the proposed m...
Programs fail mainly for two reasons: logic errors in the code, and exception failures. Exception failures can account for up to 2/3 of system crashes [6], hence are worthy of ser...