It is well known that trivariate reduction — a method to generate two dependent random variables from three independent random variables — can be used to generate Poisson rand...
Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries efficiently. Previous work such as de Salvo Braz et al.'s first-order variabl...
Brian Milch, Luke S. Zettlemoyer, Kristian Kerstin...
In the recent years particle filtering has been the dominant paradigm for tracking facial and body features, recognizing temporal events and reasoning in uncertainty. A major prob...
Uncertainty is a key issue in decision analysis and other kinds of applications. Researchers have developed a number of approaches to address computations on uncertain quantities....
Stochastic analysis techniques for real-time systems model the execution time of tasks as random variables. These techniques constitute a very powerful tool to study the behaviour...
Image synthesis algorithms are commonly compared on the basis of running times and/or perceived quality of the generated images. In the case of Monte Carlo techniques, assessment ...
—Finding the distribution of the sum of lognormal We assume in this paper that the terms Yi are independent. The random variables is an important mathematical problem in probabil...
Consider a collection of random variables attached to the vertices of a graph. The reconstruction problem requires to estimate one of them given ‘far away’ observations. Sever...
A merger is a probabilistic procedure which extracts the randomness out of any (arbitrarily correlated) set of random variables, as long as one of them is uniform. Our main result...
Majority of practical multivariate statistical analyses and optimizations model interdependence among random variables in terms of the linear correlation among them. Though linear...