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Showing 1 to 10 of 10 for “"large alphabet"”.

  1. Optimal entropy estimation on large alphabet: fundamental limits and fast algorithms

    Consider the problem of estimating the Shannon entropy of a distribution over k elements from n independent samples. We obtain the minimax mean- square error within universal multiplicative constant factors if n exceeds a constant factor of k/log(k); otherwise there exists no consistent estimator. …

    uiuc Repository record for Optimal entropy estimation on large alphabet: fundamental limits and fast algorithms (opens in a new tab)

  2. Divergence Covering

    … methods. We look at covering discrete large-alphabet probabilities both with worst-case divergence distance and average-case divergence distance and examine the implications of these divergence covering numbers. One implication of worst-case divergence covering is finding how to …

    mit Repository record for Divergence Covering (opens in a new tab)

  3. Analysis, improvement and extensions of a Lee metric list decoding algorithm for alternant codes

    … improvements in special cases are given as for large alphabet codes and codes over GF(2). Tools are developped to study Lee metric codes over a Gaussian channel with a PSK or QAM modulation and to compare Lee and Hammming metrics.

    nus Repository record for Analysis, improvement and extensions of a Lee metric list decoding algorithm for alternant codes (opens in a new tab)

  4. Construction of I-Deletion-Correcting Ternary Codes

    Finding large deletion correcting codes is an important issue in coding theory. Many researchers have studied this topic over the years. Varshamov and Tenegolts constructed the Varshamov-Tenengolts codes (VT codes) and Levenshtein showed the Varshamov-Tenengolts codes are perfect binary …

    brock Repository record for Construction of I-Deletion-Correcting Ternary Codes (opens in a new tab)

  5. The Optimal Error Resilience of Interactive Communication Over Binary Channels

    … error resilience of such a protocol over a large alphabet is well understood, the situation over the binary alphabet has remained open. Over the binary alphabet, there has remained a substantial gap in error resilience between the best protocol construction and the best known upper bound, …

    mit Repository record for The Optimal Error Resilience of Interactive Communication Over Binary Channels (opens in a new tab)

  6. Towards reliable organisms: fault-tolerance in unconventional models of computation

    … study a model of formula-based computation over larger alphabets subject to symmetric noise; and show that performing computation with large alphabet majority gates results in strictly larger nominal thresholds than achievable using Boolean majority gates. We then move away from a formula-based …

    mit Repository record for Towards reliable organisms: fault-tolerance in unconventional models of computation (opens in a new tab)

  7. High-dimensional entanglement-based quantum key distribution

    … high-dimensional QKD allows encoding onto a larger state space, such as multiple levels of a continuous variable of a single photon, thus enabling the system to achieve higher photon information efficiency (bits/photon) and potentially higher key rate (bits/second). However, its deployment …

    mit Repository record for High-dimensional entanglement-based quantum key distribution (opens in a new tab)

  8. Decision-making under statistical uncertainty

    … is particularly significant for the testing of large alphabet distributions. We also show that the test statistic used in the GLRT is a relaxation of the Kullback-Leibler divergence statistic used in the Hoeffding test. We present results on the asymptotic behavior of the two test statistics to …

    uiuc Repository record for Decision-making under statistical uncertainty (opens in a new tab)

  9. Hypothesis testing and learning with small samples

    Made available in DSpace on 2013-02-03T19:47:07Z (GMT). No. of bitstreams: 2 Dayu_Huang.pdf: 867949 bytes, checksum: 1380ac54cb65b4bf5dc75aac2c9cb8dc (MD5) license.txt: 4059 bytes, checksum: 77c2d693fd71ccd7d98454f2a06de3cb (MD5)

    uiuc Repository record for Hypothesis testing and learning with small samples (opens in a new tab)

  10. Estimation of KL divergence: optimal minimax rate

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms

    uiuc Repository record for Estimation of KL divergence: optimal minimax rate (opens in a new tab)