We consider here the case where our knowledge is partial and based on a betting density function which is n-dimensional Gaussian. The explicit formulation of the least committed b...
Francois Caron, Branko Ristic, Emmanuel Duflos, Ph...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for solving hybrid Bayesian networks. Any probability density function can be approximated...
In many real-life situations, we only have partial information about the actual probability distribution. For example, under Dempster-Shafer uncertainty, we only know the masses m...
Hidden Markov chains (HMC) are widely applied in various problems occurring in different areas like Biosciences, Climatology, Communications, Ecology, Econometrics and Finances, ...
Pseudo-independent (PI) models are a special class of probabilistic domain model (PDM) where a set of marginally independent domain variables shows collective dependency, a specia...