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2,239
A String Matching Interpretation for the ArimotoBlahut Algorithm
 In Proc. of the Sixth Canadian Workshop on Information Theory
, 1999
"... The ArimotoBlahut (AB) algorithm is an iterative procedure for computing the ratedistortion function for a given source and distortion measure. It starts with an arbitrary (strictly positive) reproduction distribution, and iteratively produces a sequence of reproduction distributions (and correspo ..."
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The ArimotoBlahut (AB) algorithm is an iterative procedure for computing the ratedistortion function for a given source and distortion measure. It starts with an arbitrary (strictly positive) reproduction distribution, and iteratively produces a sequence of reproduction distributions (and
NUMERICAL COMPUTATION OF THE CAPACITY OF CONTINUOUS MEMORYLESS CHANNELS
"... We extend the BlahutArimoto algorithm to continuous memoryless channels by means of sequential Monte Carlo integration in conjunction with gradient methods. We apply the algorithm to a Gaussian channel with an average and/or peakpower constraint. ..."
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We extend the BlahutArimoto algorithm to continuous memoryless channels by means of sequential Monte Carlo integration in conjunction with gradient methods. We apply the algorithm to a Gaussian channel with an average and/or peakpower constraint.
1Computing the Channel Capacity and Ratedistortion Function with Twosided State Information
, 2004
"... We present iterative algorithms that numerically solve optimization problems of computing the capacitypower and ratedistortion functions for coding with twosided state information. Numerical examples are provided to demonstrate efficiency of our algorithms. Key words BlahutArimoto algorithm, co ..."
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We present iterative algorithms that numerically solve optimization problems of computing the capacitypower and ratedistortion functions for coding with twosided state information. Numerical examples are provided to demonstrate efficiency of our algorithms. Key words BlahutArimoto algorithm
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk
 In Proc. of the 17th Conference on Uncertainty in Artificial Intelligence
, 2001
"... We present an iterative Markov chain Monte Carlo algorithm for computing reference priors and minimax risk for general parametric families. Our approach uses MCMC techniques based on the BlahutArimoto algorithm for computing channel capacity in information theory. We give a statistical analysis of ..."
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We present an iterative Markov chain Monte Carlo algorithm for computing reference priors and minimax risk for general parametric families. Our approach uses MCMC techniques based on the BlahutArimoto algorithm for computing channel capacity in information theory. We give a statistical analysis
A Minimally Informative Likelihood for Decision Analysis: Robustness and Illustration
 Canadian Journal Statistics
, 1999
"... Here we use a class of likelihoods which makes weak assumptions on data generating mechanisms. These likelihoods may be appropriate for data sets where it is difficult to propose physically motivated models. We give some properties of these likelihoods, showing how they can be computed numerically b ..."
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Cited by 3 (2 self)
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by use of the BlahutArimoto algorithm. Then, in the context of a data set for which no plausible physical model is apparent, we show how these likelihoods give useful inferences for the location of a distribution. The plausibility of the inferences is enhanced by the extensive robustness analysis
Concavity of Mutual Information Rate of FiniteState Channels
"... Abstract—The computation of the capacity of a finitestate channel (FSC) is a fundamental and longstanding open problem in information theory. The capacity of a memoryless channel can be effectively computed via the classical BlahutArimoto algorithm (BAA), which, however, does not apply to a gener ..."
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Abstract—The computation of the capacity of a finitestate channel (FSC) is a fundamental and longstanding open problem in information theory. The capacity of a memoryless channel can be effectively computed via the classical BlahutArimoto algorithm (BAA), which, however, does not apply to a
Convergence of a block coordinate descent method for nondifferentiable minimization
 J. OPTIM THEORY APPL
, 2001
"... We study the convergence properties of a (block) coordinate descent method applied to minimize a nondifferentiable (nonconvex) function f(x1,...,xN) with certain separability and regularity properties. Assuming that f is continuous on a compact level set, the subsequence convergence of the iterate ..."
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Cited by 290 (3 self)
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and compactness of the level set may be relaxed further. These results are applied to derive new (and old) convergence results for the proximal minimization algorithm, an algorithm of Arimoto and Blahut, and an algorithm of Han. They are applied also to a problem of blind source separation.
Optimal Parametric BackwardAdaptive Lossy Compression ∗
, 2005
"... We present a new generic mechanism for “online ” construction of a vector quantizer codebook, based on blockwise backwardadaptive parametric encoding. The workings of the proposed scheme is explained by the principle of “natural type selection”: In the limit of large vector dimension, the type of ..."
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of the first distortionmatching codeword within a random codebook coincides with an iteration of the BlahutArimoto algorithm for computation of the ratedistortion function. We extend this observation to parametric codebooks, and demonstrate that the parameter sequence converges to an optimum solution within
Calculating Probabilistic Anonymity from Sampled Data
"... This paper addresses the problem of calculating the anonymity of a system statistically from a number of trial runs. We show that measures of anonymity based on capacity can be estimated, by showing that the BlahutArimoto algorithm converges for sampled data. We obtain bounds on the error of the es ..."
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Cited by 2 (1 self)
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This paper addresses the problem of calculating the anonymity of a system statistically from a number of trial runs. We show that measures of anonymity based on capacity can be estimated, by showing that the BlahutArimoto algorithm converges for sampled data. We obtain bounds on the error
Additive NonGaussian Noise Attacks on the Scalar Costa Scheme (SCS)
"... The additive attack public mutual information game is explicitly solved for one of the simplest quantization based watermarking schemes, the scalar Costa scheme (SCS).It is a zerosum game played between the embedder and the attacker, and the payoff function is the mutual information.The solution of ..."
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of the game, a subgame perfect nash equilibrium, is found by backward induction.Therefore, the BlahutArimoto algorithm is employed for numerically optimizing the mutual information over noise distributions.Although the worst case distribution is in general strongly nonGaussian, the capacity degradation
Results 11  20
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2,239