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...
Many real life domains contain a mixture of discrete and continuous variables and can be modeled as hybrid Bayesian Networks (BNs). An important subclass of hybrid BNs are conditi...
A standard method for approximating averages in probabilistic models is to construct a Markov chain in the product space of the random variables with the desired equilibrium distr...
The subcellular location of proteins is most often determined by visual interpretation of fluorescence microscope images. In recent years, automated systems have been developed so...
Shann-Ching Chen, Geoffrey J. Gordon, Robert F. Mu...
We introduce a new approach to analyzing click logs by examining both the documents that are clicked and those that are bypassed--documents returned higher in the ordering of the ...
Atish Das Sarma, Sreenivas Gollapudi, Samuel Ieong