Prediction of Aviation Material Demand Based on Poisson Distribution and Bayesian Method

2018 ◽  
pp. 207-213
Author(s):  
Penghui Niu ◽  
Wei Hu ◽  
Zhen Wang
Keyword(s):  
Poisson Distribution ◽  
Bayesian Method ◽  
Aviation Material

scholarly journals ANALISIS SISTEM PELAYANAN KERETA API DI STASIUN SEMARANG TAWANG MENGGUNAKAN PROSES BAYESIAN

2020 ◽  
Vol 9 (4) ◽  
pp. 495-504
Author(s):  
Lifana Nugraeni ◽  
Sugito Sugito ◽  
Dwi Ispriyanti
Keyword(s):  
Poisson Distribution ◽  
Posterior Distribution ◽  
Public Transportation ◽  
Bayesian Method ◽  
Service System ◽  
Likelihood Function ◽  
Performance Standard ◽  
Sample Distribution ◽  
Service Patterns ◽  
Transportation Facilities

Along with the times, transportation has progressed. Regarding the means of transportation, one of the phenomenon that is easily encountered in everyday life is the queue at public transportation facilities. One of the queues that occurred at public transportation facilities is  the train queue at Semarang Tawang Station. The number of trains that passes the station can cause the train service at the station busy. This study aims to see whether the train service system of Semarang Tawang Station is good or not. This can be consider by the queues method, determining the distribution of arrival patterns and service patterns to obtain a queues system model and a system performance standard. In this study, the distribution of arrival patterns and service patterns are determined by calculating the posterior distribution using the Bayesian method. The bayesian method was chosen because it is able to combine the sample distribution in the current study with the previous information for the same cases. The prior distribution and the likelihood function are the elements needed to obtain the posterior distribution. The distribution of arrival patterns and service patterns obtained from previous information follows the Poisson distribution. Based on the calculation of the posterior distribution, the result shows that the distribution of the arrival pattern is a discrete uniform distribution and the distribution of the service pattern is a Poisson distribution. The result shows that the train service system at Semarang Tawang Station has a model (Uniform Discrete / Gamma / 7: GD / ~ / ~) and has good service based on the performance values obtained.

scholarly journals On the Analysis of the Truncated Generalized Poisson Distribution Using a Bayesian Method

1998 ◽  
Vol 28 (1) ◽  
pp. 135-152 ◽  
Author(s):  
David P.M. Scollnik
Keyword(s):  
Poisson Distribution ◽  
Bayesian Method ◽  
Mass Function ◽  
Probability Mass Function ◽  
Generalized Poisson Distribution ◽  
Generalized Poisson ◽  
Mean And Variance ◽  
Probability Mass ◽  
The Mean ◽  
Definition Of

AbstractThe generalized Poisson distribution with parameters θ and λ was introduced by Consul and Jain (1973) and has recently found several instances of application in the actuarial literature. The most frequently used version of the distribution assumes that θ > 0 and 0 ≤ λ < 1, in which case the mean and variance are θ/(1 − λ) and θ/(1 − λ)3, respectively. These simple moment expressions, along with nearly all of the other theoretical results available for this distribution, fail when λ < 0 or λ > 1 (e.g., Johnson, Kotz, and Kemp, 1992, page 397). In these cases, even the definition of the probability mass function usually given in the literature is not properly normalized so that its values do not sum to unity. For this reason, it is common to truncate the support of the distribution and explicitly normalize the probability mass function. In this paper we discuss the estimation of the parameters of this truncated generalized Poisson distribution using a Bayesian method.

BAYESIAN METHOD FOR RAPID MULTI-WELL INTERPRETATION OF WELL LOGS AND CORE DATA IN UNCONVENTIONAL FORMATIONS

2020 ◽  
Author(s):  
Tianqi Deng ◽  
◽  
Joaquín Ambía ◽  
Carlos Torres-Verdín ◽  
◽  
...  
Keyword(s):  
Bayesian Method ◽  
Well Logs ◽  
Core Data

Performance Comparison of Web Backend And Database: A Case Study Of Node.JS, Golang and MySQL, MongoDB

2019 ◽  
Vol 13 ◽  
Author(s):  
Faried Effendy ◽  
Taufik ◽  
Bramantyo Adhilaksono
Keyword(s):  
Response Time ◽  
Open Source ◽  
Poisson Distribution ◽  
Mobile Application ◽  
Web Applications ◽  
Performance Comparison ◽  
Memory Usage ◽  
Data Traffic ◽  
Cpu Utilization

: Substantial research has been conducted to compare web servers or to compare databases, but very limited research combines the two. Node.js and Golang (Go) are popular platforms for both web and mobile application back-ends, whereas MySQL and Go are among the best open source databases with different characters. Using MySQL and MongoDB as databases, this study aims to compare the performance of Go and Node.js as web applications back-end regarding response time, CPU utilization, and memory usage. To simulate the actual web server workload, the flow of data traffic on the server follows the Poisson distribution. The result shows that the combination of Go and MySQL is superior in CPU utilization and memory usage, while the Node.js and MySQL combination is superior in response time.

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