We present a class of models that, via a simple construction,
enables exact, incremental, non-parametric, polynomial-time,
Bayesian inference of conditional measures. The approac...
For concurrent probabilistic programs having process-level nondeterminism, it is often necessary to restrict the class of schedulers that resolve nondeterminism to obtain sound an...
Rohit Chadha, A. Prasad Sistla, Mahesh Viswanathan
We augment the I/O automaton model of Lynch and Tuttle with probability, as a step toward the ultimate goal of obtaining a useful tool for specifying and reasoning about asynchron...
Tractable subsets of first-order logic are a central topic in AI research. Several of these formalisms have been used as the basis for first-order probabilistic languages. Howev...
Abstract. This paper considers the probabilistic may/must testing theory for processes having external, internal, and probabilistic choices. We observe that the underlying testing ...