Weighted entropy: basic inequalities

Kelbert, Mark; Stuhl, Izabella; Suhov, Yuri

doi:10.15559/17-vmsta85

Cited by 13 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shannon develops the concept of information entropy to express the randomness [24]. e information entropy describes the reliability, importance, and the weight of an influential factor [25]. In order to avoid the interference of human factors, we use entropy to weight attributes.…”

Section: Weight Methods Based On Information Entropymentioning

confidence: 99%

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Zheng

Zhong

Fang

et al. 2019

Advances in Civil Engineering

View full text Add to dashboard Cite

A rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective weight of the characteristic factors, and the similarity between the samples is calculated by the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreement with the actual conditions of the subsequent construction, thus verifying the feasibility and effectiveness of the model in the rockburst prediction.

show abstract

Section: Weight Methods Based On Information Entropymentioning

confidence: 99%

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Zheng

Zhong

Fang

et al. 2019

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…For simplicity, we use p(τ g ) to represent g e p R (τ g , g e | θ), which is the occurrence probability of the goal-state trajectory τ g . The expectation is calculated based on p(τ g ) as well, so the proposed objective is the weighted entropy (Guias ¸u, 1971;Kelbert et al, 2017) of τ g , which we denote as H w p (T g ), where the weight w is the accumulated reward…”

Section: Maximum Entropy-regularized Multi-goal Rlmentioning

confidence: 99%

“…Z is the normalization factor for q(τ g ). H w p (T g ) is the weighted entropy (Guias ¸u, 1971;Kelbert et al, 2017), where the weight is the accumulated reward T t=1 r(S t , G e ), in our case.…”

Section: Surrogate Objectivementioning

confidence: 99%

Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

Zhao,

Sun,

Tresp

2019

Preprint

View full text Add to dashboard Cite

In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple goals with a goalconditioned policy. During learning, the agent first collects the trajectories into a replay buffer, and later these trajectories are selected randomly for replay. However, the achieved goals in the replay buffer are often biased towards the behavior policies. From a Bayesian perspective, when there is no prior knowledge about the target goal distribution, the agent should learn uniformly from diverse achieved goals. Therefore, we first propose a novel multi-goal RL objective based on weighted entropy. This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals. Secondly, we developed a maximum entropy-based prioritization framework to optimize the proposed objective. For evaluation of this framework, we combine it with Deep Deterministic Policy Gradient, both with or without Hindsight Experience Replay. On a set of multi-goal robotic tasks of Ope-nAI Gym, we compare our method with other baselines and show promising improvements in both performance and sample-efficiency.

show abstract

“…The notion proved useful in various applications and gave rise to many papers. We quote here but a few: Barbu et al [ 15 ], Batty [ 16 ], Das [ 17 ], Guiasu [ 18 ], Kayal [ 19 ], Kelbert et al [ 20 ], Smieja [ 21 ], Suhov [ 22 ], and Tunnicliffe et al [ 23 ], where the interested reader may find more details.…”

Section: Introductionmentioning

confidence: 99%

Weighted Relative Group Entropies and Associated Fisher Metrics

Hirica

Pripoae

et al. 2022

Entropy

View full text Add to dashboard Cite

A large family of new α-weighted group entropy functionals is defined and associated Fisher-like metrics are considered. All these notions are well-suited semi-Riemannian tools for the geometrization of entropy-related statistical models, where they may act as sensitive controlling invariants. The main result of the paper establishes a link between such a metric and a canonical one. A sufficient condition is found, in order that the two metrics be conformal (or homothetic). In particular, we recover a recent result, established for α=1 and for non-weighted relative group entropies. Our conformality condition is “universal”, in the sense that it does not depend on the group exponential.

show abstract

Weighted entropy: basic inequalities

Cited by 13 publications

References 28 publications

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

Weighted Relative Group Entropies and Associated Fisher Metrics

Contact Info

Product

Resources

About