We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...
Let A be a self-adjoint operator acting over a space X endowed with a partition. We give lower bounds on the energy of a mixed state from its distribution in the partition and the...
We define the crouching Dirichlet, hidden Markov model (CDHMM), an HMM for partof-speech tagging which draws state prior distributions for each local document context. This simple...
The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as ...
Ba-Ngu Vo, Ba-Tuong Vo, Nam-Trung Pham, David Sute...
The solution of continuous and discrete-time Markovian models is still challenging mainly when we model large complex systems, for example, to obtain performance indexes of paralle...