اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدFinding the Maximizers of the Information Divergence from an Exponential Family
180
0
0.0
(
0
)
تأليف
Johannes Rauh
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
This paper investigates maximizers of the information divergence from an exponential family $E$. It is shown that the $rI$-projection of a maximizer $P$ to $E$ is a convex combination of $P$ and a probability measure $P_-$ with disjoint support and the same value of the sufficient statistics $A$. This observation can be used to transform the original problem of maximizing $D(cdot||E)$ over the set of all probability measures into the maximization of a function $Dbar$ over a convex subset of $ker A$. The global maximizers of both problems correspond to each other. Furthermore, finding all local maximizers of $Dbar$ yields all local maximizers of $D(cdot||E)$. This paper also proposes two algorithms to find the maximizers of $Dbar$ and applies them to two examples, where the maximizers of $D(cdot||E)$ were not known before.
قيم البحث
اقرأ أيضاً
The problem to maximize the information divergence from an exponential family is generalized to the setting of Bregman divergences and suitably defined Bregman families.
The Contrastive Divergence (CD) algorithm has achieved notable success in training energy-based models including Restricted Boltzmann Machines and played a key role in the emergence of deep learning. The idea of this algorithm is to approximate the i
ntractable term in the exact gradient of the log-likelihood function by using short Markov chain Monte Carlo (MCMC) runs. The approximate gradient is computationally-cheap but biased. Whether and why the CD algorithm provides an asymptotically consistent estimate are still open questions. This paper studies the asymptotic properties of the CD algorithm in canonical exponential families, which are special cases of the energy-based model. Suppose the CD algorithm runs $m$ MCMC transition steps at each iteration $t$ and iteratively generates a sequence of parameter estimates ${theta_t}_{t ge 0}$ given an i.i.d. data sample ${X_i}_{i=1}^n sim p_{theta_star}$. Under conditions which are commonly obeyed by the CD algorithm in practice, we prove the existence of some bounded $m$ such that any limit point of the time average $left. sum_{s=0}^{t-1} theta_s right/ t$ as $t to infty$ is a consistent estimate for the true parameter $theta_star$. Our proof is based on the fact that ${theta_t}_{t ge 0}$ is a homogenous Markov chain conditional on the data sample ${X_i}_{i=1}^n$. This chain meets the Foster-Lyapunov drift criterion and converges to a random walk around the Maximum Likelihood Estimate. The range of the random walk shrinks to zero at rate $mathcal{O}(1/sqrt[3]{n})$ as the sample size $n to infty$.
Renyi divergence is related to Renyi entropy much like Kullback-Leibler divergence is related to Shannons entropy, and comes up in many settings. It was introduced by Renyi as a measure of information that satisfies almost the same axioms as Kullback
-Leibler divergence, and depends on a parameter that is called its order. In particular, the Renyi divergence of order 1 equals the Kullback-Leibler divergence. We review and extend the most important properties of Renyi divergence and Kullback-Leibler divergence, including convexity, continuity, limits of $sigma$-algebras and the relation of the special order 0 to the Gaussian dichotomy and contiguity. We also show how to generalize the Pythagorean inequality to orders different from 1, and we extend the known equivalence between channel capacity and minimax redundancy to continuous channel inputs (for all orders) and present several other minimax results.
This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel. Due to the bottleneck constraint, it is impossible for the oblivious relay to inform the destination node of the perfect cha
nnel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node can get the perfect CSI at no cost and two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.
In this paper, we introduce the Age of Incorrect Information (AoII) as an enabler for semantics-empowered communication, a newly advocated communication paradigm centered around datas role and its usefulness to the communications goal. First, we shed
light on how the traditional communication paradigm, with its role-blind approach to data, is vulnerable to performance bottlenecks. Next, we highlight the shortcomings of several proposed performance measures destined to deal with the traditional communication paradigms limitations, namely the Age of Information (AoI) and the error-based metrics. We also show how the AoII addresses these shortcomings and captures more meaningfully the purpose of data. Afterward, we consider the problem of minimizing the average AoII in a transmitter-receiver pair scenario where packets are sent over an unreliable channel subject to a transmission rate constraint. We prove that the optimal transmission strategy is a randomized threshold policy, and we propose a low complexity algorithm that finds both the optimal threshold and the randomization parameter. Furthermore, we provide a theoretical comparison between the AoII framework and the standard error-based metrics counterpart. Interestingly, we show that the AoII-optimal policy is also error-optimal for the adopted information source model. At the same time, the converse is not necessarily true. Finally, we implement our proposed policy in various real-life applications, such as video streaming, and we showcase its performance advantages compared to both the error-optimal and the AoI-optimal policies.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
