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We propose a modular neural-network structure for implementing the Bayesian framework for learning and inference. Our design has three main components, two for computing the priors and likelihoods based on observations and one for applying Bayes' rule. Through comprehensive simulations we show that our proposed model succeeds in implementing Bayesian… (More)

We use data extracted from many weblogs to identify the underlying relations of a set of companies in the Standard and Poor (S&P) 500 index. We define a pairwise similarity measure for the companies based on the weblog articles and then apply a graph clustering procedure. We show that it is possible to capture some interesting relations between companies… (More)

Bayesian models of cognition hypothesize that human brains make sense of data by representing probability distributions and applying Bayes' rule to find the best explanation for available data. Understanding the neural mechanisms underlying probabilistic models remains important because Bayesian models provide a computational framework, rather than… (More)

We propose a constructive neural-network model comprised of deterministic units which estimates and represents probability distributions from observable events — a phenomenon related to the concept of probability matching. We use a form of operant learning, where the underlying probabilities are learned from positive and negative reinforcements of the… (More)

—We study the problem of decentralized detection in wireless sensor networks. First, we will review the classical framework for decentralized detection. The classical framework does not take into account the power constraints of wireless sensor networks and also the characteristics of wireless channels such as fading and noise. Next, we will review the… (More)