The Markov decision process model is a powerful tool in planing tasks and sequential decision making problems. The randomness of state transitions and rewards implies that the performance of a policy is often stochastic. In contrast to the standard approach that studies the expected performance, we consider the policy that maximizes the probability of achieving a pre-determined target performan...

Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. On the other hand, the field of Recommender Systems is a fertile research area w...

We construct a class of real-valued nonnegative binary functions on a set of jointly distributed random variables, which satisfy the triangle inequality and vanish at identical arguments (pseudo-quasi-metrics). We apply these functions to the problem of selective probabilistic causality encountered in behavioral sciences and in quantum physics. The problem reduces to that of ascertaining the ex...

By studying quantum information masking in non-Hermitian systems, we show that mutually orthogonal states can be deterministically masked, while an arbitrary set of cannot masked systems. We further demonstrate a linearly independent which are $\eta$-orthogonal by pseudo-unitary operator. Moreover, study robustness against noisy environments. The deterministic and probabilistic under different ...

in this paper, a new enhanced version of the particle swarm optimization (pso) is presented. an important modification is made by adding probabilistic functions into pso, and it is named probabilistic particle swarm optimization (ppso). since the variation of the velocity of particles in pso constitutes its search engine, it should provide two phases of optimization process which are: explorati...

Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filteri...

We describe properties of a process calculus that has been developed for the purpose of analyzing security protocols. The process calculus is a restricted form of π-calculus, with bounded replication and probabilistic polynomial-time expressions allowed in messages and boolean tests. In order to avoid problems expressing security in the presence of nondeterminism, messages are scheduled probabi...

Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. Our approach uses a probabilistic approach in fusing the noisy sensor data and learning ...

In recent years, it has become increasingly clear that the vision of the Semantic Web requires uncertain reasoning over rich, firstorder representations. Markov logic brings the power of probabilistic modeling to first-order logic by attaching weights to logical formulas and viewing them as templates for features of Markov networks. This gives natural probabilistic semantics to uncertain or eve...

To establish if the uniform number generator implemented in the TreeAge software delivers the expected outcome we investigated the probabilistic and statistical properties of pseudo uniform random numbers generated with TreeAge in several ways. We computed the first four empirical central and noncentral moments of a uniform sequence generated with TreeAge and compared them to their theoretical ...