Treffer: Bayesian Inference and Prediction in an M/G/1 with Optional Second Service
Title:
Bayesian Inference and Prediction in an M/G/1 with Optional Second Service
Authors:
Source:
Communications in statistics. Simulation and computation. 41(3-5):419-435
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Théorie des probabilités et processus stochastiques, Probability theory and stochastic processes, Processus stochastiques, Stochastic processes, Statistiques, Statistics, Inférence paramétrique, Parametric inference, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Chaîne Markov, Markov chain, Cadena Markov, Estimation Bayes, Bayes estimation, Estimación Bayes, Estimation densité, Density estimation, Estimación densidad, Estimation statistique, Statistical estimation, Estimación estadística, Fonction répartition, Distribution function, Función distribución, Loi normale, Gaussian distribution, Curva Gauss, Loi probabilité, Probability distribution, Ley probabilidad, Mélange loi probabilité, Mixed distribution, Mezcla ley probabilidad, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode statistique, Statistical method, Método estadístico, Probabilité, Probability, Probabilidad, Processus Poisson, Poisson process, Proceso Poisson, Processus service, Service process, Proceso servicio, Processus stochastique, Stochastic process, Proceso estocástico, Prédiction, Prediction, Predicción, Queue M/G/1, M/G/1 queue, Cola M/G/1, Simulation numérique, Numerical simulation, Simulación numérica, Temps service, Service time, Tiempo servicio, Théorie filtrage, Filtering theory, Théorie prédiction, Prediction theory, 60E05, 60G25, 60J80, 62F15, 62M20, Estimation paramétrique, 60K25, Birth-death MCMC, Optional service, Truncated normal mixture
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Johann Bernoulli Institute, University of Groningen, Groningen, Netherlands
Department of Statistics, Allameh Tabataba'i University, Tehran, Iran, Islamic Republic of
Department of Statistics, Allameh Tabataba'i University, Tehran, Iran, Islamic Republic of
ISSN:
0361-0918
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.25439920
Database:
PASCAL Archive
Weitere Informationen
In this article, we exploit the Bayesian inference and prediction for an M/G/l queuing model with optional second re-service. In this model, a service unit attends customers arriving following a Poisson process and demanding service according to a general distribution and some of customers need to re-service with probability p. First, we introduce a mixture of truncated Normal distributions on interval (—∞,0) to approximate the service and re-service time densities. Then, given observations of the system, we propose a Bayesian procedure based on birth-death MCMC methodology to estimate some performance measures. Finally, we apply the theories in practice by providing a nunterical example based on real data which have been obtained from a hospital.