We introduce a general concept of probabilistic interventions in Bayesian networks. This generalizes deterministic interventions, which fix nodes to certain states. We propose “pushing” variables in the direction of target states without fixing them. We formalize this idea in a Bayesian framework based on Conditional Gaussian networks.

The proof of a schemata theorem for trees of bounded branching with a probabilistic crossover is sketched. The argument presented applies only to the case where the genetic information is placed at the terminal nodes of the tree although the shape of the tree itself can be considered as an additional form of genetic information.

Packet Scale Rate Guarantee (PSRG) is a node model used by IETF for Expedited Forwarding, a priority service defined in the context of Differentiated Services [10]. We review probabilistic performance bounds that apply to PSRG nodes, and improve a previous bound for loss probability.

Sensor nodes are low power, tiny, and computationally restricted microelectromechanical devices that usually run on battery. They are capable of communicating over short distances and of sensing information for specific purposes. In sensor networks, large amount of sensor nodes are deployed over a wide region. For secure communication among sensor nodes, secure links must be established via key...

the current study investigated and compared data generation methods for groundwater modeling. thesemethods can be divided into two classes; geostatistic and probabilistic. by comparing geostatistic methods,the best method was chosen and the hydraulic conductivity of the aquifer was generated for a study area ina grid (9-cell) format. after observing weak spatial correlation between the data, th...

3 Probabilistic model for computing connection strength 5 3.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 Independent edge existence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2.1 General formulae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2.2 Computing path conn...

Systems that are subject to uncertainty in their behaviour are often modelled by Bayesian Belief Networks (BBNs). These are probabilistic models of the system in which the independence relations between the variables of interest are represented explicitly. A directed graph is used, in which two nodes are connected by an edge if one is a 'direct cause'

− A mixture probabilistic Principal Component Analysis (PCA) model is proposed as a multivariate process monitoring tool in this paper. High dimensional measurement data could be aggregated into some clusters based on the mixture distribution model, where the number of these clusters is automatically determined by the maximum likelihood estimation procedure. The multivariate statistical process...

We study ensemble-based graph-theoretical methods aiming to approximate the size of the minimum dominating set (MDS) in scale-free networks. We analyze both analytical upper bounds of dominating sets and numerical realizations for applications. We propose two novel probabilistic dominating set selection strategies that are applicable to heterogeneous networks. One of them obtains the smallest p...

This paper describes formal probabilistic models of flooding and gossiping protocols, and explores the influence of different modelling choices and assumptions on the results of performance analysis. We use Prism, a model checker for probabilistic systems, for the formal analysis of protocols and small network topologies, and use in addition MonteCarlo simulation, implemented in Matlab, to esta...