Entropy on covers

Wang, Zhimin

doi:10.1007/s10618-011-0230-1

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…We use the news sentiment analysis module to classify the sentiment of MC. This module 43 was trained using thousands of manually labeled news corpus, and the accuracy of emotion classification is as high as 85%–90%. According to the pre‐training model, this module performs weighted analysis on the sentiment vocabulary appearing in MC, and the sentiment analysis result is presented as a real number ranging from 0 to 1.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Timely and effective media coverage's role in the spread of Corona Virus Disease 2019

Wang

Qing

et al. 2022

Math Methods in App Sciences

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For all humanity, the sudden outbreak of Corona Virus Disease 2019 has been an important problem. Timely and effective media coverage is considered to be one of the effective approaches to control the spread of epidemic in early stage. In this paper, a Sentiment‐enabled Susceptible‐Exposed‐Infected‐Recovered (SEIR) model is established to reveal the relationship between the propagation of the epidemic and media coverage. The authors take the positive and negative media coverage into consideration when implementing the Sentiment‐enabled SEIR model. This model is constructed by parameterizing the number of current confirmed cases, cumulative cured cases, cumulative deaths, and media coverage. The numerical simulation and sensitivity analysis are conducted based on the Sentiment‐enabled SEIR model. The numerical analysis confirms the rationality of the Sentiment‐enabled SEIR model. The sensitivity analysis shows that positive media coverage acts a pivotal part in reducing the figure for confirmed cases. Negative media coverage has an effect on the figure for confirmed cases is not as significant as that of positive media coverage, but it is not negligible.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Timely and effective media coverage's role in the spread of Corona Virus Disease 2019

Wang

Qing

et al. 2022

Math Methods in App Sciences

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Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento

Horta¹

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Bayesian Optimal Experimental Design for Inferring Causal Structure

Zemplenyi

Miller

2023

Bayesian Anal.

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Inferring the causal structure of a system typically requires interventional data, rather than just observational data. Since interventional experiments can be costly, it is preferable to select interventions that yield the maximum amount of information about a system. We propose a novel Bayesian method for optimal experimental design by sequentially selecting interventions that minimize the expected posterior entropy as rapidly as possible. A key feature is that the method can be implemented by computing simple summaries of the current posterior, avoiding the computationally burdensome task of repeatedly performing posterior inference on hypothetical future datasets drawn from the posterior predictive. After deriving the method in a general setting, we apply it to the problem of inferring causal networks. We present a series of simulation studies, in which we find that the proposed method performs favorably compared to existing alternative methods. Finally, we apply the method to real data from two gene regulatory networks.

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Entropy on covers

Cited by 3 publications

References 23 publications

Timely and effective media coverage's role in the spread of Corona Virus Disease 2019

Timely and effective media coverage's role in the spread of Corona Virus Disease 2019

Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento

Bayesian Optimal Experimental Design for Inferring Causal Structure

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