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We show how variational Bayesian inference can be implemented for very large binary classification generalized linear models. Our relaxation is shown to be a convex problem for any log-concave model, and we provide an efficient double loop algorithm for so ...
The framework of graphical models is a cornerstone of applied Statistics, allowing for an intuitive graphical specification of the main features of a model, and providing a basis for general Bayesian inference computations though belief propagation (BP). I ...
In this paper, we propose a novel approach for solving the reliable broadcast problem in a probabilistic model, i.e., where links lose messages and where processes crash and recover probabilistically. Our approach consists in first defining the optimality ...
Thanks to their different senses, human observers acquire multiple information coming from their environment. Complex cross-modal interactions occur during this perceptual process. This article proposes a framework to analyze and model these interactions t ...
In this paper, we will present an efficient approach for distributed inference. We use belief propagation's message-passing algorithm on top of a DHT storing a Bayesian network. Nodes in the DHT run a variant of the spring relaxation algorithm to redistrib ...
I present an introduction to some of the concepts within Bayesian networks to help a beginner become familiar with this field's theory. Bayesian networks are a combination of two different mathematical areas: graph theory and probability theory. So, I firs ...
Serialization is the process that transforms the state of a software object into a sequence of bytes. Serialization is useful, for example, to store the value of an object in persistent memory or to send it through a network channel. Ada provides object se ...
In this paper, we propose a novel approach for solving the reliable broadcast problem in a probabilistic unreliable model. Our approach consists in first defining the optimality of probabilistic reliable broadcast algorithms and the adaptiveness of algorit ...
Serialization is the process that transforms the state of a software object into a sequence of bytes. Serialization is useful, for example, to store the value of an object in persistent memory or to send it through a network channel. Ada provides object se ...
In this paper, we propose a Bayesian network framework for managing interactivity between a tour-guide robot and visitors in mass exhibition conditions, through robust interpretation of multi-modal signals. We report on methods and experiments interpreting ...
