Monte Carlo comparisons of estimators for pH non-linearity

Monte Carlo methods are used to compare the performance of four non-linear estimators when applied to the logarithmic pH non-linearity. The estimators are: direct inversion of the non-linearity, Bayes'rule, extended Kalman filter, statistical linearisation. It is found that the unbiased optimal...

Full description

Saved in:
Bibliographic Details
Published in:Transactions of the Institute of Measurement and Control 1984-10, Vol.6 (6), p.287-292
Main Authors: Jacobs, O.L.R., Briggs, M.S.
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Estimates
Estimators
Inversions
Monte Carlo methods
Nonlinearity
Optimization
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c1462-490f09aa2e571a53924251dd0e796be7f2d28f460c020e023e1d45e0a658424a3
container_end_page 292
container_issue 6
container_start_page 287
container_title Transactions of the Institute of Measurement and Control
container_volume 6
creator Jacobs, O.L.R.
Briggs, M.S.
description Monte Carlo methods are used to compare the performance of four non-linear estimators when applied to the logarithmic pH non-linearity. The estimators are: direct inversion of the non-linearity, Bayes'rule, extended Kalman filter, statistical linearisation. It is found that the unbiased optimal estimates of Bayes' rule are in many cases significantly better than the other estimates. It is concluded that the Bayes algorithm could be useful for practical purposes.
doi_str_mv 10.1177/014233128400600602
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_760215489</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sage_id>10.1177_014233128400600602</sage_id><sourcerecordid>24276536</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1462-490f09aa2e571a53924251dd0e796be7f2d28f460c020e023e1d45e0a658424a3</originalsourceid><addsrcrecordid>eNp9kDFPwzAQhS0EEqXwB5gywRR6ts92woYqSpGKWGC2TGKjVKkd7HTov8dR2ZAqnXTL997de4TcUnigVKkFUGScU1YhgJyGnZEZRaVK4LI-J7MJKCfiklyltAUARIkz8vgW_GiLpYl9KJqwG0zsUvCpCK6waex2ZgwxFS7EYlgXPviy77zN0Hi4JhfO9Mne_O05-Vw9fyzX5eb95XX5tCkbipKVWIOD2hhmhaJG8JohE7RtwapaflnlWMsqhxIaYGCBcUtbFBaMFBUyNHxO7o--Qww_-_yU3nWpsX1vvA37pFVOSwVWdSbvTpL5spKCywyyI9jEkFK0Tg8xR40HTUFPher_hWbR4ihK5tvqbdhHn2OfUvwC9_hzMg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>24276536</pqid></control><display><type>article</type><title>Monte Carlo comparisons of estimators for pH non-linearity</title><source>SAGE Complete Deep Backfile Purchase 2012</source><creator>Jacobs, O.L.R. ; Briggs, M.S.</creator><creatorcontrib>Jacobs, O.L.R. ; Briggs, M.S.</creatorcontrib><description>Monte Carlo methods are used to compare the performance of four non-linear estimators when applied to the logarithmic pH non-linearity. The estimators are: direct inversion of the non-linearity, Bayes'rule, extended Kalman filter, statistical linearisation. It is found that the unbiased optimal estimates of Bayes' rule are in many cases significantly better than the other estimates. It is concluded that the Bayes algorithm could be useful for practical purposes.</description><identifier>ISSN: 0142-3312</identifier><identifier>EISSN: 1477-0369</identifier><identifier>DOI: 10.1177/014233128400600602</identifier><language>eng</language><publisher>Thousand Oaks, CA: SAGE Publications</publisher><subject>Algorithms ; Bayesian analysis ; Estimates ; Estimators ; Inversions ; Monte Carlo methods ; Nonlinearity ; Optimization</subject><ispartof>Transactions of the Institute of Measurement and Control, 1984-10, Vol.6 (6), p.287-292</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1462-490f09aa2e571a53924251dd0e796be7f2d28f460c020e023e1d45e0a658424a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/014233128400600602$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/014233128400600602$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,780,784,21845,27924,27925,45082,45470</link.rule.ids></links><search><creatorcontrib>Jacobs, O.L.R.</creatorcontrib><creatorcontrib>Briggs, M.S.</creatorcontrib><title>Monte Carlo comparisons of estimators for pH non-linearity</title><title>Transactions of the Institute of Measurement and Control</title><description>Monte Carlo methods are used to compare the performance of four non-linear estimators when applied to the logarithmic pH non-linearity. The estimators are: direct inversion of the non-linearity, Bayes'rule, extended Kalman filter, statistical linearisation. It is found that the unbiased optimal estimates of Bayes' rule are in many cases significantly better than the other estimates. It is concluded that the Bayes algorithm could be useful for practical purposes.</description><subject>Algorithms</subject><subject>Bayesian analysis</subject><subject>Estimates</subject><subject>Estimators</subject><subject>Inversions</subject><subject>Monte Carlo methods</subject><subject>Nonlinearity</subject><subject>Optimization</subject><issn>0142-3312</issn><issn>1477-0369</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1984</creationdate><recordtype>article</recordtype><recordid>eNp9kDFPwzAQhS0EEqXwB5gywRR6ts92woYqSpGKWGC2TGKjVKkd7HTov8dR2ZAqnXTL997de4TcUnigVKkFUGScU1YhgJyGnZEZRaVK4LI-J7MJKCfiklyltAUARIkz8vgW_GiLpYl9KJqwG0zsUvCpCK6waex2ZgwxFS7EYlgXPviy77zN0Hi4JhfO9Mne_O05-Vw9fyzX5eb95XX5tCkbipKVWIOD2hhmhaJG8JohE7RtwapaflnlWMsqhxIaYGCBcUtbFBaMFBUyNHxO7o--Qww_-_yU3nWpsX1vvA37pFVOSwVWdSbvTpL5spKCywyyI9jEkFK0Tg8xR40HTUFPher_hWbR4ihK5tvqbdhHn2OfUvwC9_hzMg</recordid><startdate>198410</startdate><enddate>198410</enddate><creator>Jacobs, O.L.R.</creator><creator>Briggs, M.S.</creator><general>SAGE Publications</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>7SP</scope><scope>L7M</scope></search><sort><creationdate>198410</creationdate><title>Monte Carlo comparisons of estimators for pH non-linearity</title><author>Jacobs, O.L.R. ; Briggs, M.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1462-490f09aa2e571a53924251dd0e796be7f2d28f460c020e023e1d45e0a658424a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1984</creationdate><topic>Algorithms</topic><topic>Bayesian analysis</topic><topic>Estimates</topic><topic>Estimators</topic><topic>Inversions</topic><topic>Monte Carlo methods</topic><topic>Nonlinearity</topic><topic>Optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jacobs, O.L.R.</creatorcontrib><creatorcontrib>Briggs, M.S.</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Transactions of the Institute of Measurement and Control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jacobs, O.L.R.</au><au>Briggs, M.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monte Carlo comparisons of estimators for pH non-linearity</atitle><jtitle>Transactions of the Institute of Measurement and Control</jtitle><date>1984-10</date><risdate>1984</risdate><volume>6</volume><issue>6</issue><spage>287</spage><epage>292</epage><pages>287-292</pages><issn>0142-3312</issn><eissn>1477-0369</eissn><abstract>Monte Carlo methods are used to compare the performance of four non-linear estimators when applied to the logarithmic pH non-linearity. The estimators are: direct inversion of the non-linearity, Bayes'rule, extended Kalman filter, statistical linearisation. It is found that the unbiased optimal estimates of Bayes' rule are in many cases significantly better than the other estimates. It is concluded that the Bayes algorithm could be useful for practical purposes.</abstract><cop>Thousand Oaks, CA</cop><pub>SAGE Publications</pub><doi>10.1177/014233128400600602</doi><tpages>6</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0142-3312
ispartof Transactions of the Institute of Measurement and Control, 1984-10, Vol.6 (6), p.287-292
issn 0142-3312
1477-0369
language eng
recordid cdi_proquest_miscellaneous_760215489
source SAGE Complete Deep Backfile Purchase 2012
subjects Algorithms
Bayesian analysis
Estimates
Estimators
Inversions
Monte Carlo methods
Nonlinearity
Optimization
title Monte Carlo comparisons of estimators for pH non-linearity
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T12%3A11%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Monte%20Carlo%20comparisons%20of%20estimators%20for%20pH%20non-linearity&rft.jtitle=Transactions%20of%20the%20Institute%20of%20Measurement%20and%20Control&rft.au=Jacobs,%20O.L.R.&rft.date=1984-10&rft.volume=6&rft.issue=6&rft.spage=287&rft.epage=292&rft.pages=287-292&rft.issn=0142-3312&rft.eissn=1477-0369&rft_id=info:doi/10.1177/014233128400600602&rft_dat=%3Cproquest_cross%3E24276536%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c1462-490f09aa2e571a53924251dd0e796be7f2d28f460c020e023e1d45e0a658424a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=24276536&rft_id=info:pmid/&rft_sage_id=10.1177_014233128400600602&rfr_iscdi=true