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Bayesian Mixture Modeling by Monte Carlo Simulation
, 1991
"... . It is shown that Bayesian inference from data modeled by a mixture distribution can feasibly be performed via Monte Carlo simulation. This method exhibits the true Bayesian predictive distribution, implicitly integrating over the entire underlying parameter space. An infinite number of mixture com ..."
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Cited by 35 (0 self)
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. It is shown that Bayesian inference from data modeled by a mixture distribution can feasibly be performed via Monte Carlo simulation. This method exhibits the true Bayesian predictive distribution, implicitly integrating over the entire underlying parameter space. An infinite number of mixture components can be accommodated without difficulty, using a prior distribution for mixing proportions that selects a reasonable subset of components to explain any finite training set. The need to decide on a "correct" number of components is thereby avoided. The feasibility of the method is shown empirically for a simple classification task. Introduction Mixture distributions [8, 20] are an appropriate tool for modeling processes whose output is thought to be generated by several different underlying mechanisms, or to come from several different populations. One aim of a mixture model analysis may be to identify and characterize these underlying "latent classes" [2, 7], either for some scient...
A maximum likelihood approach to blind multiuser interference cancellation
 IEEE Trans. Signal Process
, 2001
"... Abstract—This paper addresses the problem of blind multiple access interference (MAI) and intersymbol interference (ISI) suppression in direct sequence code division multiple access (DS CDMA) systems. A novel approach to obtain the coefficients of a linear receiver using the maximum likelihood (ML) ..."
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Cited by 6 (0 self)
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Abstract—This paper addresses the problem of blind multiple access interference (MAI) and intersymbol interference (ISI) suppression in direct sequence code division multiple access (DS CDMA) systems. A novel approach to obtain the coefficients of a linear receiver using the maximum likelihood (ML) principle is proposed. The method is blind because it only exploits the statistical features of the transmitted symbols and Gaussian noise in the channel. We demonstrate that an adequate linear constraint on these coefficients ensures that the desired user is extracted and the resulting linearly constrained maximum likelihood linear (LCMLL) receiver can be efficiently implemented using the iterative space alternating generalized expectation–maximization (SAGE) algorithm. In order to take advantage of the diversity inherent to multipath channels, we also introduce a blind rake multiuser receiver that proceeds in two steps. First, soft estimates of the desired user transmitted symbols are obtained from each propagation path using a bank of appropiate LCMLL receivers. Afterwards, these estimates are adequately combined to enhance the signaltointerferenceandnoise ratio (SINR). Computer simulations show that the proposed blind algorithms for multiuser detection are near–far resistant and attain convergence using small blocks of data, thus outperforming existing linearly constrained minimum variance (LCMV) blind receivers. Index Terms—Blind receivers, CDMA, interference suppression, maximum likelihood, multiuser detection, rake receiver. I.
Ž. Journal of Empirical Finance 7 2000 479–507
, 2000
"... www.elsevier.comrlocatereconbase Macroeconomic announcement effects on the covariance structure of government bond returns ..."
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www.elsevier.comrlocatereconbase Macroeconomic announcement effects on the covariance structure of government bond returns
Decisionfeedback interference suppression in CDMA systems: a MLbased semiblind approach
, 2003
"... This paper addresses the problem of interference suppression in direct sequence code division multiple access systems. We propose a novel semiblind decision feedback (DF) receiver based on the maximum likelihood principle that simultaneously exploits the transmission of training sequences and the st ..."
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This paper addresses the problem of interference suppression in direct sequence code division multiple access systems. We propose a novel semiblind decision feedback (DF) receiver based on the maximum likelihood principle that simultaneously exploits the transmission of training sequences and the statistical information of the unknown transmitted symbols. Both iterative and adaptive implementations of the proposed receiver, derived within the framework of the expectation maximization algorithm, are presented. Computer simulations show that the resulting multiuser detectors attain practically the same performance as the theoretical DF minimum mean square error receiver.