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Bootstrap Markov chain Monte Carlo and optimal solutions for the Law of Categorical Judgment (Corrected)

Abstract:

A novel procedure is described for accelerating the convergence of Markov chain Monte Carlo computations. The algorithm uses an adaptive bootstrap technique to generate candidate steps in the Markov Chain. It is efficient for symmetric, convex probability distributions, similar to multivariate Gaussians, and it can be used for Bayesian estimation or for obtaining maximum likelihood solutions with confidence limits. As a test case, the Law of Categorical Judgment (Corrected) was fitted with th...

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Publication status:
Published
Peer review status:
Not peer reviewed
Version:
Author's Original

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Institution:
University of Oxford
Research group:
Phonetics Laboratory
Department:
Humanities Division - Linguistics & Phonetics - Phonetics
Role:
Author
More by this author
Institution:
University of Oxford
Research group:
Phonetics Laboratory
Department:
Humanities Division - Linguistics & Phonetics - Phonetics
Role:
Author
Publication date:
2010-01-01
URN:
uuid:99e3b72f-2226-4a63-9c45-f8e1263ba762
Local pid:
ora:4614

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