Elements of information theory (2. ed.)

By Thomas M. Cover, Joy A. Thomas. 2006

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Citations in the corpus

Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction (2018, background): “On the other hand, the proof of Fano’s inequality in [15] can be extended easily to obtain (see, also, [20]) Pr [”
Privacy Aware Learning (2012, background): “4 by Cover and Thomas [2006].) In our case, since Q∗ does not satisfy this equality, the uniqueness of Q∗ as the minimizer of I(P∗, Q∗) will follow if we show that Q∗ is a minimizer at all.”
Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations (2011, neutral): “A related measure is the entropy rate [13].”
Information-Theoretic Lower Bounds on Bayes Risk in Decentralized Estimation (2016, methodology): “When W ∈ R d and (cid:96) ( w, (cid:98) w ) = (cid:107) w − (cid:98) w r for any norm (cid:107)·(cid:107) in R d and any r ≥ 1 , The proof of Theorem 5 is inspired by the proof of the Slepian-Wolf converse for distributed almost-lossless source coding using the cutset argument [26, Chap.”
New And Surprising Ways to Be Mean (2018, background): “For an introduction to the information-theoretic notions see [13], and [8] for a detailed introduction to empowerment.”
Wireless Device-to-Device Caching Networks: Basic Principles and System Performance (2013, background): “case, scalable video coding can be coupled with some form of broadcast channel coding [12].”
Modelling high-frequency limit order book dynamics with support vector machines (2015, methodology): “Therefore, we select a smaller set of attributes in the feature space, which we can call an ‘economized feature set’, selected according to their contributions to the performance of the resulting model as measured in terms of information gain (Föllmer 1973, Cover and Thomas 2006).”
Sparse PCA: Optimal rates and adaptive estimation (2012, background): “The proofs rely on results in [17, 19, 20, 25, 31, 33] and [51].”
Scoring functions for cryoEM density fitting. (2011, background): “Mutual information is an entropy-based concept that measures the amount of information one random variable contains about another random variable (Cover and Thomas, 2006).”
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation (2008, background): “Then the average of their log probabilities will be very close to H, the negative entropy (Cover and Thomas 2006), because H is simply the mean log probability: / H Sql(x) ln (ql(x)): Thus, for ‘‘typical’’ sites whose log probabilities are close to this mean, we obtain ql(x):e H.”
Limits of Reliable Communication with Low Probability of Detection on AWGN Channels (2012, methodology): “…is γ 2 nR and that, since each message is equiprobable, the entropy of a message W from U is H ( W ) = log 2 |U| = log 2 γ + nR , we utilize (16) and carry out steps (9.38)–(9.53) in [7] to obtain: Since Alice transmits ω ( √ n ) bits in n channel uses, her rate is R = ω (1 / √ n ) bits/symbol.”
Physical-layer network coding: Tutorial, survey, and beyond (2011, unknown): “This study falls into the domain of network information theory [14].”
Models for Time-varying Moments Using Maximum Entropy Applied to a Generalized Measure of Volatility (2008, methodology): “…find the conditional density function f for xt as solution to the problem max f ( − ∫ z∈D f(z)log(f(z))dz ) under the constraints that∫ z∈D f(z)dz = 1 and ∫ z∈D gi(z)f(z)dz = mi,t i ∈ {1, ..., k}, where D is the support for xt. Solutions to this problem can be found e.g. in Cover and Thomas (2006).”
Information-Based Complexity, Feedback and Dynamics in Convex Programming (2010, unknown): “When N = 2 , we use a stronger form of Fano’s inequality (see, e.g., Section 2.10 in [26]): (cid:55)→ we get h 2 ( δ ) ≥ log 2 − I ( M ; ˆ M T ) . the Rearranging, we get (22).”
Multiuser Two-Way Amplify-and-Forward Relay Processing and Power Control Methods for Beamforming Systems (2010, methodology): “The sum rate is , where the rate of the th user is deﬁned as SNR by using a point-to-point communication rate [33].”
Cognitive Control (2012, unknown): “In information theory’s terminology, that might be understood to mean a probabilistic representation with minimum entropy due to the fact that entropy is a measure of uncertainty about a random variable [19], [39].”