Federated learning encapsulates distributed strategies that are managed by a central unit. Since it relies on using selected number of agents at each iteration, and since agent, in turn, taps into its local data, is only natural to study optimal sampling policies for selecting their data federated implementations. Usually, uniform schemes used. However, this work, we examine the effect importan...

Many applications in signal processing and machine learning require the study of probability density functions (pdfs) that can only be accessed through noisy evaluations. In this work, we analyze importance sampling (IS), i.e., IS working with evaluations target density. We present general framework derive optimal proposal densities for estimators. The proposals incorporate information variance...

What happens when one sets up a computer simulation using Importance Sampling theory and then, during the simulation, knowingly or unknowingly, generates the simulation uncertainty (e.g. channel noise) with statistics other than those assumed in the simulation setup? By exploring this simple question, we nd some interesting aspects of Importance Sampling that provide insight into its properties...

Bayesian networks (BNs) offer a compact, intuitive, and efficient graphical representation of uncertain relationships among the variables in a domain and have proven their value in many disciplines over the last two decades. However, two challenges become increasingly critical in practical applications of Bayesian networks. First, real models are reaching the size of hundreds or even thousands ...

Abstract. Simulated annealing — moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions — has traditionally been used as an inexact method of handling isolated modes in Markov chain samplers. Here, it is shown how one can use the Markov chain transitions for such an annealing sequence to define an importance sampler. The Markov chain aspec...