Abstract. We present an efficient probabilistic workflow for the estimation of source parameters induced seismic events in three-dimensional heterogeneous media. Our exploits a linearized variant Hamiltonian Monte Carlo (HMC) algorithm. Compared to traditional Markov chain (MCMC) algorithms, HMC is highly sampling high-dimensional model spaces. Through linearization forward problem around prior...

We introduce a robust approach to diagnostic information fusion within a network of probabilistic models distributed throughout a system of agents. In particular, we introduce an algorithm for inter-agent belief propagation, which combines partial fusion results from local probabilistic models in a consistent manner. This approach allows sequential fusion of large amounts of heterogeneous infor...

Probabilistic distance functions, including several variants of value difference metrics, minimum risk metric and ShortFukunaga metrics, are used with prototype-based rules (P-rules) to provide a very concise and comprehensible classification model. Application of probabilistic metrics to nominal or discrete features is straightforward. Heterogeneous metrics that handle continuous attributes wi...

This report describes the riso project, a system for uniied prediction and diagnosis in HVAC systems based on a class of probabilistic models called belief networks. Progress has been made in both theoretical and practical problems: a scheme for the representation of belief networks with heterogeneous conditional distributions has been devised, an algorithm for inference in a polytree network w...

The idea of probabilistic metric space was introduced by Menger and he showed that probabilistic metric spaces are generalizations of metric spaces. Thus, in this paper, we prove some of the important features and theorems and conclusions that are found in metric spaces. At the beginning of this paper, the distance distribution functions are proposed. These functions are essential in defining p...

Abstract The availability of multi-omics data has revolutionized the life sciences by creating avenues for integrated system-level approaches. Data integration links information across datasets to better understand underlying biological processes. However, high dimensionality, correlations and heterogeneity pose statistical computational challenges. We propose a general framework, probabilistic...

the notion of a probabilistic metric  space  corresponds to thesituations when we do not know exactly the  distance.  probabilistic metric space was  introduced by karl menger. alsina, schweizer and sklar gave a general definition of  probabilistic normed space based on the definition of menger [1]. in this note we study the pn spaces which are  topological vector spaces and the open mapping an...

In this paper, the connection between Menger probabilistic norms and H"{o}hle probabilistic norms is discussed. In addition, the correspondence between probabilistic norms and Wu-Fang fuzzy (semi-) norms is established. It is shown that a probabilistic norm (with triangular norm $min$) can generate a Wu-Fang fuzzy semi-norm and conversely, a Wu-Fang fuzzy norm can generate a probabilistic norm.