Entropy Bayesian Analysis for the Generalized Inverse Exponential Distribution Based on URRSS

Al-Omari, Amer Ibrahim; Hassan, Amal S.; Nagy, Heba F.; Alanzi, Ayed R. A.; Alzoubi, Loai

doi:10.32604/cmc.2021.019061

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Due to that, we used the Markov chain Monte Carlo technique (MCMC) to find approximate values of integrals ( 35 ). Many studies have used the MCMC technique, such as El-Sherpieny et al [ 26 ], Almongy et al [ 27 ], Haj Ahmad et al [ 28 ], Bantan et al [ 29 ], Almetwally et al [ 24 ], Al-Omari et al [ 30 ], Al-Babtain et al [ 31 ], and Hassan and Zaki [ 32 ].…”

Section: Parameter Estimationmentioning

confidence: 99%

Reliability Analysis of the New Exponential Inverted Topp–Leone Distribution with Applications

Metwally

Hassan

Almetwally

et al. 2021

Entropy

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The inverted Topp–Leone distribution is a new, appealing model for reliability analysis. In this paper, a new distribution, named new exponential inverted Topp–Leone (NEITL) is presented, which adds an extra shape parameter to the inverted Topp–Leone distribution. The graphical representations of its density, survival, and hazard rate functions are provided. The following properties are explored: quantile function, mixture representation, entropies, moments, and stress–strength reliability. We plotted the skewness and kurtosis measures of the proposed model based on the quantiles. Three different estimation procedures are suggested to estimate the distribution parameters, reliability, and hazard rate functions, along with their confidence intervals. Additionally, stress–strength reliability estimators for the NEITL model were obtained. To illustrate the findings of the paper, two real datasets on engineering and medical fields have been analyzed.

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Section: Parameter Estimationmentioning

confidence: 99%

Reliability Analysis of the New Exponential Inverted Topp–Leone Distribution with Applications

Metwally

Hassan

Almetwally

et al. 2021

Entropy

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“…Bayesian networks are a combination of AI and several theories, such as a graph, decision, and probability that uses Bayesian inference for probability computations. Bayesian inference is used in many studies such as [8,9] that help to infer missing or unknown data.…”

Section: Introductionmentioning

confidence: 99%

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

El‐Saleh¹,

Alhammadi²,

Shayea³

et al. 2022

Computers, Materials &Amp; Continua

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Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultralow latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS) of video streaming services for any particular cellular network. The MOS is the most commonly used metric to assess the quality of experience. The proposed Bayesian model consists of several input data, namely, round-trip time, stalling load, and bite rates. It was examined and evaluated using several test data sizes with various performance metrics. Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network. The proposed Bayesian network obtained a remarkable overall accuracy of 90.36% and outperformed the neural network.

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Analysis of information measures using generalized type-Ⅰ hybrid censored data

Helmy

Hassan

El-Kholy

et al. 2023

MATH

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<abstract><p>An entropy measure of uncertainty has a complementary dual function called extropy. In the last six years, this measure of randomness has gotten a lot of attention. It cannot, however, be applied to systems that have survived for some time. As a result, the idea of residual extropy was created. To estimate the extropy and residual extropy, Bayesian and non-Bayesian estimators of unknown parameters of the exponentiated gamma distribution are generated. Bayesian estimators are regarded using balanced loss functions like the balanced squared error, balanced linear exponential and balanced general entropy. We use the Lindley method to get the extropy and residual extropy estimates for the exponentiated gamma distribution based on generalized type-Ⅰ hybrid censored data. To test the effectiveness of the proposed methodologies, a simulation experiment was carried out, and the actual data set was studied for illustrative purposes. In summary, the mean squared error values decrease as the number of failures increases, according to the results obtained. The Bayesian estimates of residual extropy under the balanced linear exponential loss function perform well compared to the other estimates. Alternatively, the Bayesian estimates of the extropy perform well under a balanced general entropy loss function in the majority of situations.</p></abstract>

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Entropy Bayesian Analysis for the Generalized Inverse Exponential Distribution Based on URRSS

Cited by 4 publications

References 29 publications

Reliability Analysis of the New Exponential Inverted Topp–Leone Distribution with Applications

Reliability Analysis of the New Exponential Inverted Topp–Leone Distribution with Applications

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

Analysis of information measures using generalized type-Ⅰ hybrid censored data

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