Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting first-order specificat...
We study two-layer belief networks of binary random variables in which the conditional probabilities Pr childjparents depend monotonically on weighted sums of the parents. In larg...
: Probabilistic Boolean Network (PBN) is widely used to model genetic regulatory networks. Evolution of the PBN is according to the transition probability matrix. Steady-state (lon...
Shuqin Zhang, Wai-Ki Ching, Michael K. Ng, Tatsuya...
In this work, we show that the current termination condition of the Probabilistic Packet Marking (PPM) algorithm is not correct for general networks, and this implies the estimati...
Abstract. Imaging is a class of non-Bayesian methods for the revision of probability density functions originally proposed as a semantics for conditional logic. Two of these revisi...