We propose novel spatio-temporal models to estimate clickthrough rates in the context of content recommendation. We track article CTR at a fixed location over time through a dynam...
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
In this paper, we consider the relationship between risksensitivity and information. Product estimators are introduced as a generalization of Maximum A Posteriori Probability (MAP...
Vahid Reza Ramezani, Steven I. Marcus, Michael C. ...
Background: The currently used kth order Markov models estimate the probability of generating a single nucleotide conditional upon the immediately preceding (gap = 0) k units. How...
One of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. Previous research has generally represented ...