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Bayesian parameter estimation: A Monte Carlo approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2001) 2073 812-822
DOI: 10.1007/3-540-45545-0_93
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Abstract

This paper presents a Bayesian approach, using parallel Monte Carlo modelling algorithms for combining expert judgements when there is inherent variability amongst these judgements. The proposed model accounts for the situation when the derivative method for finding the maximum likelihood breaks down.

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Gallagher, R., & Doran, T. (2001). Bayesian parameter estimation: A Monte Carlo approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2073, pp. 812–822). Springer Verlag. https://doi.org/10.1007/3-540-45545-0_93

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