: We develop and study the concept of dataflow process networks as used for example by Kahn to suit exact computation over data types related to real numbers, such as continuous fu...
Real-world networks often need to be designed under uncertainty, with only partial information and predictions of demand available at the outset of the design process. The field ...
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
We present algorithms for a class of resource allocation problems both in the online setting with stochastic input and in the offline setting. This class of problems contains man...
Nikhil R. Devanur, Kamal Jain, Balasubramanian Siv...
Within this paper a new framework for Bayesian tracking is presented, which approximates the posterior distribution at multiple resolutions. We propose a tree-based representation...
Bjoern Stenger, Arasanathan Thayananthan, Philip H...