We consider a distributed detection problem where measurements at each sensor follow general parametric distribution. The network does not have central processing unit or fusion center (FC). Thus, node takes some measurements, processing, exchanges messages with its neighbors and finally makes decision (typically the same for all nodes) about phenomenon of interest. can be formulated as composi...

In a peer-to-peer inference system, there is no centralized control or hierarchical organization: each peer is equivalent in functionality and cooperates with other peers in order to solve a collective reasoning task. Since peer theories model possibly different viewpoints, even if each local theory is consistent, the global theory may be inconsistent. We exhibit a distributed algorithm detecti...

This paper analyses how different types of access regulation to next generation networks affect investments and consumer welfare. The model consists of an investment stage with uncertain returns and subsequent quantity competition. The access price is a function of investment costs and the regulatory regime. A regime with fully distributed costs or regulatory holiday induces highest investments...

Spatially distributed process models represent soil-water-plant-nutrient interactions within a watershed. We investigated spatial patterns of water balance components and nitrate transport simulated using the fully AgES (Agricultural Ecosystems Services) semi-distributed SWAT (Soil Water Assessment Tool, 2012) in Big Dry Creek Watershed, mixed agricultural suburban watershed semi-arid Colorado,...

Overview In contrast to the classical linear regression model, in which the components of the dependent variable vector y are not identically distributed (because its mean vector varies with the regressors) but may be independently distributed, time series models have dependent variables which may be identically distributed, but are typically not independent across ovbservations. Such models ar...

Overview In contrast to the classical linear regression model, in which the components of the dependent variable vector y are not identically distributed (because its mean vector varies with the regressors) but may be independently distributed, time series models have dependent variables which may be identically distributed, but are typically not independent across ovbservations. Such models ar...

Overview In contrast to the classical linear regression model, in which the components of the dependent variable vector y are not identically distributed (because its mean vector varies with the regressors) but may be independently distributed, time series models have dependent variables which may be identically distributed, but are typically not independent across ovbservations. Such models ar...