In this paper we address the challenging problem of multiple source localization in Wireless Sensor Networks (WSN). We develop an efficient statistical algorithm, based on the novel application of Sequential Monte Carlo (SMC) sampler methodology, that is able to deal with an unknown number of sources given quantized data obtained at the fusion center from different sensors with imperfect wirele...

In this article we discuss recent work on coarse-graining methods for microscopic stochastic lattice systems. We emphasize the numerical analysis of the schemes, focusing on error quantification as well as on the construction of improved algorithms capable of operating in wider parameter regimes. We also discuss adaptive coarse-graining schemes which have the capacity of automatically adjusting...

With Bayesian logic networks (BLNs), we present a practical representation formalism for statistical relational knowledge. Based on the concept of mixed networks with probabilistic and deterministic constraints, BLNs combine the probabilistic semantics of (relational) Bayesian networks with constraints in first-order logic. In practical applications, efficient inference in statistical relationa...

background the lifestyle from the point of sociology refers to the way a person lives to one’s own ability but basically and due to its importance with health and wellness means choosing the best method for improving health aspects in one person’s life. lifestyle is discussed in different religious as well as several countries. objectives due to the importance of studying about lifestyle as a w...

An importance sampling (IS) simulation method is presented for evaluating the lower-bound symbol error rate (SER) of the Bayesian decision feedback equalizer (DFE) with M -PAM symbols, under the assumption of correct decision feedback. By exploiting an asymptotic property of the Bayesian DFE, a design procedure is developed, which chooses appropriate bias vectors for the simulation density to e...

Statistical Model Checking (SMC) is a technique, based on Monte-Carlo simulations, for computing the bounded probability that a specific event occurs during a stochastic system’s execution. Estimating the probability of a “rare” event accurately with SMC requires many simulations. To this end, Importance Sampling (IS) is used to reduce the simulation effort. Commonly, IS involves “tilting” the ...

Likelihood ratio policy gradient methods have been some of the most successful reinforcement learning algorithms, especially for learning on physical systems. We describe how the likelihood ratio policy gradient can be derived from an importance sampling perspective. This derivation highlights how likelihood ratio methods under-use past experience by (i) using the past experience to estimate on...

In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the importance sampling performances, as measured by an entropy criterion. The method is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student...

Aggregate scattering operators (ASOs) describe the overall scattering behavior of an asset (i.e., an object or volume, or collection thereof) accounting for all orders of its internal scattering. We propose a practical way to precompute and compactly store ASOs and demonstrate their ability to accelerate path tracing. Our approach is modular avoiding costly and inflexible scene-dependent precom...