Loading…

Objective Bayesian Analysis for the Differential Entropy of the Gamma Distribution

The present paper introduces a fully objective Bayesian analysis to obtain the posterior distribution of an entropy measure. Notably, we consider the gamma distribution, which describes many natural phenomena in physics, engineering, and biology. We reparametrize the model in terms of entropy, and d...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-11
Main Authors: Ramos, Eduardo, Egbon, Osafu A, Ramos, Pedro L, Rodrigues, Francisco A, Louzada, Francisco
Format: Article
Language:English
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Ramos, Eduardo
Egbon, Osafu A
Ramos, Pedro L
Rodrigues, Francisco A
Louzada, Francisco
description The present paper introduces a fully objective Bayesian analysis to obtain the posterior distribution of an entropy measure. Notably, we consider the gamma distribution, which describes many natural phenomena in physics, engineering, and biology. We reparametrize the model in terms of entropy, and different objective priors are derived, such as Jeffreys prior, reference prior, and matching priors. Since the obtained priors are improper, we prove that the obtained posterior distributions are proper and that their respective posterior means are finite. An intensive simulation study is conducted to select the prior that returns better results regarding bias, mean square error, and coverage probabilities. The proposed approach is illustrated in two datasets: the first relates to the Achaemenid dynasty reign period, and the second describes the time to failure of an electronic component in a sugarcane harvest machine.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2473581166</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2473581166</sourcerecordid><originalsourceid>FETCH-proquest_journals_24735811663</originalsourceid><addsrcrecordid>eNqNjUsKwjAUAIMgWLR3CLgutEl_Wz9Vd4K4L6m84CttUvNSobe3iAdwNYsZmAULhJRJVKZCrFhI1MZxLPJCZJkM2O3atPDw-Aa-VxMQKsN3RnUTIXFtHfdP4EfUGhwYj6rjlfHODhO3-uvOqu_VXJB32IwerdmwpVYdQfjjmm1P1f1wiQZnXyOQr1s7uvlBtUgLmZVJkufyv-oD6Vk_tw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2473581166</pqid></control><display><type>article</type><title>Objective Bayesian Analysis for the Differential Entropy of the Gamma Distribution</title><source>Publicly Available Content Database</source><creator>Ramos, Eduardo ; Egbon, Osafu A ; Ramos, Pedro L ; Rodrigues, Francisco A ; Louzada, Francisco</creator><creatorcontrib>Ramos, Eduardo ; Egbon, Osafu A ; Ramos, Pedro L ; Rodrigues, Francisco A ; Louzada, Francisco</creatorcontrib><description>The present paper introduces a fully objective Bayesian analysis to obtain the posterior distribution of an entropy measure. Notably, we consider the gamma distribution, which describes many natural phenomena in physics, engineering, and biology. We reparametrize the model in terms of entropy, and different objective priors are derived, such as Jeffreys prior, reference prior, and matching priors. Since the obtained priors are improper, we prove that the obtained posterior distributions are proper and that their respective posterior means are finite. An intensive simulation study is conducted to select the prior that returns better results regarding bias, mean square error, and coverage probabilities. The proposed approach is illustrated in two datasets: the first relates to the Achaemenid dynasty reign period, and the second describes the time to failure of an electronic component in a sugarcane harvest machine.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Bayesian analysis ; Biology ; Electronic components ; Entropy ; Entropy (Information theory) ; Probability distribution functions ; Statistical analysis ; Statistical mechanics ; Sugarcane</subject><ispartof>arXiv.org, 2023-11</ispartof><rights>2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2473581166?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>780,784,25752,37011,44589</link.rule.ids></links><search><creatorcontrib>Ramos, Eduardo</creatorcontrib><creatorcontrib>Egbon, Osafu A</creatorcontrib><creatorcontrib>Ramos, Pedro L</creatorcontrib><creatorcontrib>Rodrigues, Francisco A</creatorcontrib><creatorcontrib>Louzada, Francisco</creatorcontrib><title>Objective Bayesian Analysis for the Differential Entropy of the Gamma Distribution</title><title>arXiv.org</title><description>The present paper introduces a fully objective Bayesian analysis to obtain the posterior distribution of an entropy measure. Notably, we consider the gamma distribution, which describes many natural phenomena in physics, engineering, and biology. We reparametrize the model in terms of entropy, and different objective priors are derived, such as Jeffreys prior, reference prior, and matching priors. Since the obtained priors are improper, we prove that the obtained posterior distributions are proper and that their respective posterior means are finite. An intensive simulation study is conducted to select the prior that returns better results regarding bias, mean square error, and coverage probabilities. The proposed approach is illustrated in two datasets: the first relates to the Achaemenid dynasty reign period, and the second describes the time to failure of an electronic component in a sugarcane harvest machine.</description><subject>Bayesian analysis</subject><subject>Biology</subject><subject>Electronic components</subject><subject>Entropy</subject><subject>Entropy (Information theory)</subject><subject>Probability distribution functions</subject><subject>Statistical analysis</subject><subject>Statistical mechanics</subject><subject>Sugarcane</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNjUsKwjAUAIMgWLR3CLgutEl_Wz9Vd4K4L6m84CttUvNSobe3iAdwNYsZmAULhJRJVKZCrFhI1MZxLPJCZJkM2O3atPDw-Aa-VxMQKsN3RnUTIXFtHfdP4EfUGhwYj6rjlfHODhO3-uvOqu_VXJB32IwerdmwpVYdQfjjmm1P1f1wiQZnXyOQr1s7uvlBtUgLmZVJkufyv-oD6Vk_tw</recordid><startdate>20231113</startdate><enddate>20231113</enddate><creator>Ramos, Eduardo</creator><creator>Egbon, Osafu A</creator><creator>Ramos, Pedro L</creator><creator>Rodrigues, Francisco A</creator><creator>Louzada, Francisco</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20231113</creationdate><title>Objective Bayesian Analysis for the Differential Entropy of the Gamma Distribution</title><author>Ramos, Eduardo ; Egbon, Osafu A ; Ramos, Pedro L ; Rodrigues, Francisco A ; Louzada, Francisco</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_24735811663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Bayesian analysis</topic><topic>Biology</topic><topic>Electronic components</topic><topic>Entropy</topic><topic>Entropy (Information theory)</topic><topic>Probability distribution functions</topic><topic>Statistical analysis</topic><topic>Statistical mechanics</topic><topic>Sugarcane</topic><toplevel>online_resources</toplevel><creatorcontrib>Ramos, Eduardo</creatorcontrib><creatorcontrib>Egbon, Osafu A</creatorcontrib><creatorcontrib>Ramos, Pedro L</creatorcontrib><creatorcontrib>Rodrigues, Francisco A</creatorcontrib><creatorcontrib>Louzada, Francisco</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Databases</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ramos, Eduardo</au><au>Egbon, Osafu A</au><au>Ramos, Pedro L</au><au>Rodrigues, Francisco A</au><au>Louzada, Francisco</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Objective Bayesian Analysis for the Differential Entropy of the Gamma Distribution</atitle><jtitle>arXiv.org</jtitle><date>2023-11-13</date><risdate>2023</risdate><eissn>2331-8422</eissn><abstract>The present paper introduces a fully objective Bayesian analysis to obtain the posterior distribution of an entropy measure. Notably, we consider the gamma distribution, which describes many natural phenomena in physics, engineering, and biology. We reparametrize the model in terms of entropy, and different objective priors are derived, such as Jeffreys prior, reference prior, and matching priors. Since the obtained priors are improper, we prove that the obtained posterior distributions are proper and that their respective posterior means are finite. An intensive simulation study is conducted to select the prior that returns better results regarding bias, mean square error, and coverage probabilities. The proposed approach is illustrated in two datasets: the first relates to the Achaemenid dynasty reign period, and the second describes the time to failure of an electronic component in a sugarcane harvest machine.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2023-11
issn 2331-8422
language eng
recordid cdi_proquest_journals_2473581166
source Publicly Available Content Database
subjects Bayesian analysis
Biology
Electronic components
Entropy
Entropy (Information theory)
Probability distribution functions
Statistical analysis
Statistical mechanics
Sugarcane
title Objective Bayesian Analysis for the Differential Entropy of the Gamma Distribution
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T21%3A59%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Objective%20Bayesian%20Analysis%20for%20the%20Differential%20Entropy%20of%20the%20Gamma%20Distribution&rft.jtitle=arXiv.org&rft.au=Ramos,%20Eduardo&rft.date=2023-11-13&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2473581166%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_24735811663%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2473581166&rft_id=info:pmid/&rfr_iscdi=true