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...

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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
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Summary: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.
ISSN:0142-3312
1477-0369
DOI:10.1177/014233128400600602