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Iterative decoding of binary block and convolutional codes
 IEEE TRANS. INFORM. THEORY
, 1996
"... Iterative decoding of twodimensional systematic convolutional codes has been termed “turbo” (de)coding. Using loglikelihood algebra, we show that any decoder can he used which accepts soft inputsincluding a priori valuesand delivers soft outputs that can he split into three terms: the soft chann ..."
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Cited by 609 (43 self)
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channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the loglikelihood domain are given not only for convolutional codes hut also for any linear binary systematic block code. The iteration is controlled by a
Dirichletmultinomial loglikelihood function
, 2014
"... An efficient algorithm for accurate computation of the ..."
Distortion (IS), LogLikelihood Ratio
"... Segmental SNR (Signal to Noise Ratio) is considered to be a reasonable measure of perceptual quality of speech. However it only reflects the distortion in time dependent contour of the signal due to noise. Objective Measures such as Log ..."
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Segmental SNR (Signal to Noise Ratio) is considered to be a reasonable measure of perceptual quality of speech. However it only reflects the distortion in time dependent contour of the signal due to noise. Objective Measures such as Log
On tests for global maximum of the loglikelihood function
 IEEE Trans. Inform. Theory
, 2004
"... Abstract — Given the location of a relative maximum of the loglikelihood function, how to assess whether it is the global maximum? This paper investigates a statistical tool, which answers this question by posing it as a hypothesis testing problem. A general framework for constructing tests for glo ..."
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Cited by 3 (1 self)
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Abstract — Given the location of a relative maximum of the loglikelihood function, how to assess whether it is the global maximum? This paper investigates a statistical tool, which answers this question by posing it as a hypothesis testing problem. A general framework for constructing tests
LogLikelihood Ratio (LLR) Conversion Schemes in
 IEEE Communications Letters
, 2003
"... Loglikelihood ratio (LLR) conversion schemes are proposed to reduce the effect of perforations that occur in orthogonal code hopping multiplexing (OCHM), which was previously proposed to accommodate more downlink channels than the number of orthogonal codewords. The proposed LLR conversion schemes ..."
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Loglikelihood ratio (LLR) conversion schemes are proposed to reduce the effect of perforations that occur in orthogonal code hopping multiplexing (OCHM), which was previously proposed to accommodate more downlink channels than the number of orthogonal codewords. The proposed LLR conversion schemes
LVCSR LOGLIKELIHOOD RATIO SCORING FOR KEYWORD SPOTTING
"... A new scoring algorithm has been developed for generating wordspotting hypotheses and their associated scores. This technique uses a largevocabulary continuous speech recognition (LVCSR) system to generate the Nbest answers along with their Viterbi alignments. The score for a putative hit is comput ..."
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is computed by summing the likelihoods for all hypotheses that contain the keyword normalized by dividing by the sum of all hypothesis likelihoods in the Nbest list. Using a test set of conversational speech from Switchboard Credit Card conversations, we achieved an 81 % figure of merit (FOM). Our word
Discriminative Learning of Bayesian Networks via Factorized Conditional LogLikelihood
"... We propose an efficient and parameterfree scoring criterion, the factorized conditional loglikelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional loglikelihood criterion. The approximation is devised in order to guarantee decomposa ..."
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Cited by 9 (0 self)
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We propose an efficient and parameterfree scoring criterion, the factorized conditional loglikelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional loglikelihood criterion. The approximation is devised in order to guarantee
Negative LogLikelihood And Statistical Hypothesis Testing As The Basis Of Model Selection In IDEAs
, 2000
"... In this paper, we analyze the most prominent features in model selection criteria that have been used so far in iterated density estimation evolutionary algorithms (IDEAs, EDAs, PMBGAs). These algorithms build probabilistic models and estimate probability densities based upon a selection of avail ..."
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Cited by 4 (2 self)
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of available points. We show that the negative log{likelihood is a basis of the inference features when the Kullback{Leibler divergence is used. We show how previously found to be problematic issues in the case of continuous random variables can be resolved by starting from the derived basics. By doing so
Asymptotic Characterization of LogLikelihood Maximization Based Algorithms and Applications
, 2003
"... The asymptotic distribution of estimates that are based on a suboptimal search for the maximum of the loglikelihood function is considered. In particular, estimation schemes that are based on a twostage approach, in which an initial estimate is used as the starting point of a subsequent local ..."
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The asymptotic distribution of estimates that are based on a suboptimal search for the maximum of the loglikelihood function is considered. In particular, estimation schemes that are based on a twostage approach, in which an initial estimate is used as the starting point of a subsequent
Asymptotic Distribution of LogLikelihood Maximization Based Algorithms and Applications
"... Abstract. The asymptotic distribution of estimates that are based on a suboptimal search for the maximum of the loglikelihood function is considered. In particular, estimation schemes that are based on a twostage approach, in which an initial estimate is used as the starting point of a subsequent ..."
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Abstract. The asymptotic distribution of estimates that are based on a suboptimal search for the maximum of the loglikelihood function is considered. In particular, estimation schemes that are based on a twostage approach, in which an initial estimate is used as the starting point of a subsequent
Results 1  10
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127,118