We study uncertainty models in sequential pattern mining. We consider situations where there is uncertainty either about a source or an event. We show that both these types of uncertainties could be modelled using probabilistic databases, and give possible-worlds semantics for both. We then describe ”interestingness” criteria based on two notions of frequentness (previously studied for frequent...

We show that the problem of reaching a state set with probability 1 in probabilisticnondeterministic systems operating in parallel is EXPTIME-complete. We then show that this probabilistic reachability problem is EXPTIME-complete also for probabilistic timed automata.

Probabilistic computing with p-bits is a powerful, unique paradigm alternative to classical and holds experimental advantages over certain forms of quantum computing. Stochastic nanodevices have been experimentally demonstrated act as artificial neurons in solving problems through probabilistic Still, many open questions about the breadth size soluble remain. We demonstrate capability made stoc...

Abstract We consider the quantitative problem of obtaining lower-bounds on probability termination a given non-deterministic probabilistic program. Specifically, non-termination threshold $$p \in [0, 1],$$ p ∈ [ 0 , 1 ...

The aim of the present paper is to prove that the family of all closed nonempty subsets of a complete probabilistic metric space L is complete with respect to the probabilistic Pompeiu-Hausdorff metric H . The same is true for the families of all closed bounded, respectively compact, nonempty subsets of L. If L is a complete random normed space in the sense of Šerstnev, then the family of all n...

A complete and decidable propositional logic for reasoning about states of probabilistic sequential programs is presented. The state logic is then used to obtain a sound Hoare-style calculus for basic probabilistic sequential programs. The Hoare calculus presented herein is the first probabilistic Hoare calculus with a complete and decidable state logic that has truth-functional propositional (...

In this paper, we propose a new definition of intuitionistic fuzzyquasi-metric and pseudo-metric spaces based on intuitionistic fuzzy points. Weprove some properties of intuitionistic fuzzy quasi- metric and pseudo-metricspaces, and show that every intuitionistic fuzzy pseudo-metric space is intuitionisticfuzzy regular and intuitionistic fuzzy completely normal and henceintuitionistic fuzzy nor...

Many computational problems related to probabilistic networks are complete for complexity classes that have few ’real world’ complete problems. For example, the decision variant of the inference problem (pr) is PP-complete, the map-problem is nppp-complete and deciding whether a network is monotone in mode or distribution is conppp-complete. We take a closer look at monotonicity; more specific,...

Plate-based probabilistic models combine a few relational constructs with Bayesian networks, so as to allow one to specify large and repetitive probabilistic networks in a compact and intuitive manner. In this paper we investigate the combined, data and domain complexity of plate models, showing that they range from polynomial to #P-complete to #EXP-complete.