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Bayesian Computations for Random Environment Models
This paper deals with the analysis of reliability data from a Bayesian perspective for Random Environment (RE) models. We give an overview of current literature on RE models. We also study the computational problems associated with the implementations of RE models in a Bayesian setting. Then, we pre...
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| Published in: | Journal of applied statistics 2004-07, Vol.31 (6), p.645-659 |
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| Format: | Article |
| Language: | English |
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| container_end_page | 659 |
| container_issue | 6 |
| container_start_page | 645 |
| container_title | Journal of applied statistics |
| container_volume | 31 |
| creator | Al-Mutairi, D. K. |
| description | This paper deals with the analysis of reliability data from a Bayesian perspective for Random Environment (RE) models. We give an overview of current literature on RE models. We also study the computational problems associated with the implementations of RE models in a Bayesian setting. Then, we present the Markov Chain Monte Carlo technique to solve such problems. These problems arise in posterior and predictive analysis and their relevant quantities such as mean, variance, and median. The suggested methodology is incorporated with an illustration. |
| doi_str_mv | 10.1080/1478881042000214631 |
| format | article |
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K.</creator><creatorcontrib>Al-Mutairi, D. K.</creatorcontrib><description>This paper deals with the analysis of reliability data from a Bayesian perspective for Random Environment (RE) models. We give an overview of current literature on RE models. We also study the computational problems associated with the implementations of RE models in a Bayesian setting. Then, we present the Markov Chain Monte Carlo technique to solve such problems. These problems arise in posterior and predictive analysis and their relevant quantities such as mean, variance, and median. 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K.</creatorcontrib><title>Bayesian Computations for Random Environment Models</title><title>Journal of applied statistics</title><description>This paper deals with the analysis of reliability data from a Bayesian perspective for Random Environment (RE) models. We give an overview of current literature on RE models. We also study the computational problems associated with the implementations of RE models in a Bayesian setting. Then, we present the Markov Chain Monte Carlo technique to solve such problems. These problems arise in posterior and predictive analysis and their relevant quantities such as mean, variance, and median. The suggested methodology is incorporated with an illustration.</description><subject>Bayesian analysis</subject><subject>Bayesian Computation</subject><subject>Bayesian Inference</subject><subject>Gibbs Sampling</subject><subject>Joint Prior Distribution</subject><subject>Markov analysis</subject><subject>Random Environment</subject><issn>0266-4763</issn><issn>1360-0532</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><recordid>eNqFkE9PGzEUxC1EJQLlE3BZcV9q-6299gUJIvpPqZAqOFsva6_YKGsvtpOSb1-nQZxQOYznMvPz0xBywegVo4p-YU2rlGK04ZRSzhoJ7IjMGEhaUwH8mMwol7JuWgkn5DSlVYkpJmBG4BZ3Lg3oq3kYp03GPASfqj7E6jd6G8bqzm-HGPzofK5-BevW6TP51OM6ufNXPyOPX-8e5t_rxf23H_ObRd2BlrnWrJWctl0rOwdoOXYA2lomhJCKCs2XaBVqwfYXQ9u0VjqNS8uZ7HGpGjgjlwfuFMPzxqVsVmETffnScAat5upfCA6hLoaUouvNFIcR484wavbjmHfGKa2fh1Z0k-veKhn7FU4po9kaQGDl2RWVYlNsKJJF094bYaTQ5imPBXZ9gA2-7DbinxDXtrB26xD7iL4bkoH_X6M_BLzTM_klw18WXZRx</recordid><startdate>20040701</startdate><enddate>20040701</enddate><creator>Al-Mutairi, D. 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K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian Computations for Random Environment Models</atitle><jtitle>Journal of applied statistics</jtitle><date>2004-07-01</date><risdate>2004</risdate><volume>31</volume><issue>6</issue><spage>645</spage><epage>659</epage><pages>645-659</pages><issn>0266-4763</issn><eissn>1360-0532</eissn><abstract>This paper deals with the analysis of reliability data from a Bayesian perspective for Random Environment (RE) models. We give an overview of current literature on RE models. We also study the computational problems associated with the implementations of RE models in a Bayesian setting. Then, we present the Markov Chain Monte Carlo technique to solve such problems. These problems arise in posterior and predictive analysis and their relevant quantities such as mean, variance, and median. 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| identifier | ISSN: 0266-4763 |
| ispartof | Journal of applied statistics, 2004-07, Vol.31 (6), p.645-659 |
| issn | 0266-4763 1360-0532 |
| language | eng |
| recordid | cdi_proquest_journals_213792884 |
| source | EBSCOhost Business Source Ultimate; Taylor and Francis Science and Technology Collection |
| subjects | Bayesian analysis Bayesian Computation Bayesian Inference Gibbs Sampling Joint Prior Distribution Markov analysis Random Environment |
| title | Bayesian Computations for Random Environment Models |
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