We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested the computation of model-free upper lower bounds for their arbitrage-free prices. work completely realistic setting, that only assume knowledge traded prices other single- multi-asset even allow presence bid–ask spread these provide fundamental theorem asset pricing this market model, as well superhedging duality result, allows to transform abstract maximization problem over...

Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result biased and inaccurate estimation decision making. In paper, we propose novel trust-based mixture Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior accurately estimate field. We develop Markov chain Monte Carlo (MCMC)-based algorithm...

Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result biased and inaccurate estimation decision making. We propose novel trust-based mixture Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior accurately estimate field. develop Markov chain Monte Carlo (MCMC)-based algorithm efficiently perform...

The cyber risk insurance market is at a nascent stage of its development, even as the magnitude losses significant and rate events increasing. Existing products well academic studies have been focusing on classifying developing models these events, but little attention has paid to proposing transfer strategies that incentivize mitigation loss through adjusting premium product.To address this important gap, we develop Bonus-Malus model for insurance. Specifically, propose mathematical...

Spatial regression of random fields based on potentially biased sensing information is proposed in this paper. One major concern such applications that since it not known a-priori what the accuracy collected data from each sensor is, performance can be negatively affected if fused appropriately. For example, collector may measure phenomenon inappropriately, or alternatively, sensors could out calibration, thus introducing gain and bias to measurement process. Such readings would...

We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested the computation of model-free upper lower bounds for their arbitrage-free prices. work completely realistic setting, that only assume knowledge traded prices other single- multi-asset derivatives, even allow presence bid-ask spread these provide fundamental theorem asset pricing this market model, as well superhedging duality result, allows to transform abstract maximization...

Spatial regression of random fields based on potentially biased sensing information is proposed in this paper. One major concern such applications that since it not known a-priori what the accuracy collected data from each sensor is, performance can be negatively affected if fused appropriately. For example, collector may measure phenomenon inappropriately, or alternatively, sensors could out calibration, thus introducing gain and bias to measurement process. Such readings would...

We propose a numerical algorithm for the computation of multi-marginal optimal transport (MMOT) problems involving general measures that are not necessarily discrete. By developing relaxation scheme in which marginal constraints replaced by finitely many linear and proving specifically tailored duality result this setting, we approximate MMOT problem semi-infinite optimization problem. Moreover, able to recover feasible approximately solution problem, its sub-optimality can be controlled...

We propose a numerical algorithm for computing approximately optimal solutions of the matching teams problem. Our is efficient problems involving large number agent categories and allows non-discrete type measures. Specifically, we parametrize so-called transfer functions develop parametric version dual formulation, which tackle to produce feasible primal formulations. These yield upper lower bounds value, difference between these provides direct sub-optimality estimate computed solutions....

We develop a new model for spatial random field reconstruction of binary-valued phenomenon. In our model, sensors are deployed in wireless sensor network across large geographical region. Each measures non-Gaussian inhomogeneous temporal process which depends on the Two types employed: one collects point observations at specific time points, while other integral over intervals. Subsequently, transmit these time-series to Fusion Center (FC), and FC infers phenomenon from observations. show...

We consider a general class of two-stage distributionally robust optimization (DRO) problems which includes prominent instances such as task scheduling, the assemble-to-order system, and supply chain network design. The ambiguity set is constrained by fixed marginal distributions that are not necessarily discrete. develop numerical algorithm for computing approximately optimal solutions problems. Through replacing constraints finite collection linear constraints, we derive relaxation DRO...

The cyber risk insurance market is at a nascent stage of its development, even as the magnitude losses significant and rate loss events increasing. Existing products well academic studies have been focusing on classifying developing models these events, but little attention has paid to proposing transfer strategies that incentivise mitigation through adjusting premium product. To address this important gap, we develop Bonus-Malus model for insurance. Specifically, propose mathematical...