This paper considers the computational hardness of computing expected outcomes and deciding almost–sure termination of probabilistic programs. We show that deciding almost–sure termination and deciding whether the expected outcome of a program equals a given rational value is Π2–complete. Computing lower and upper bounds on the expected outcome is shown to be recursively enumerable and Σ2–compl...

We characterize the complete monotonicity of Kilbas-Saigo function on negative half-line. also provide exact asymptotics at −∞, and uniform hyperbolic bounds are derived. The same questions addressed for classical Le Roy function. main ingredient proof is a probabilistic representation these functions in terms stable subordinator.

In this paper, the authors combine the methods of probabilistic databases, GIS (Geographical Information Systems) and business process modelling to evaluate the agricultural activities. The authors provide a technique to evaluate the risk of agricultural activities among the hydrological objects (lakes, rivers, etc.) and agricultural plots. The spatial relation is an important information neede...

Probabilistic-based methods have been used for designing noise tolerant circuits recently. In these methods, however, there is not any reliability mechanism that is essential for nanometer digital VLSI circuits. In this paper, we propose a novel method for designing reliable probabilistic-based logic gates. The advantage of the proposed method in comparison with previous probabilistic-based met...

We present an exhaustive analysis of the problem of computing the relative entropy of two probabilistic automata. We show that the problem of computing the relative entropy of unambiguous probabilistic automata can be formulated as a shortest-distance problem over an appropriate semiring, give efficient exact and approximate algorithms for its computation in that case, and report the results of...

The problem of efficient evaluation of queries on probabilistic databases has received a great attention in recent years. In this thesis, we introduce novel classes of queries with inequalities (<,≤) that admit tractable evaluation, in the context where query evaluation is #P-hard in general. We also introduce a new kind of hard query that is not captured by previous characterizations. As an ap...

Hierarchical probabilistic automata (HPA) are probabilistic automata whose states are partitioned into levels such that for any state and input symbol, at most one transition with non-zero probability goes to a state at the same level, and all others go to states at a higher level. We present expressiveness and decidability results for 1-level HPAs that work on both finite and infinite length i...

Probabilistic Databases (PDBs) extend traditional relational databases by annotating each record with a weight, or a probability. Although PDBs define a very simple probability space, by simply adding constraints one can model much richer probability spaces, such as those represented by Markov Logic Networks or other Statistical Relational Models. While in traditional databases query evaluation...

We have developed an algorithm called Q5 for probabilistic classification of healthy versus disease whole serum samples using mass spectrometry. The algorithm employs principal components analysis (PCA) followed by linear discriminant analysis (LDA) on whole spectrum surface-enhanced laser desorption/ionization time of flight (SELDI-TOF) mass spectrometry (MS) data and is demonstrated on four r...