We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Background: Development of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms...
Distributed Perception Networks (DPN) are a MAS approach to large scale fusion of heterogeneous and noisy information. DPN agents can establish meaningful information filtering c...
This work casts the traffic analysis of anonymity systems, and in particular mix networks, in the context of Bayesian inference. A generative probabilistic model of mix network ar...
The vision-based scene understanding technique that infers scene-interpreting contexts from real-world vision data has to not only deal with various uncertain environments but also...