Working paper icon

Working paper

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...

Expand abstract
Publication status:
Published
Peer review status:
Not peer reviewed

Actions


Access Document


Files:

Authors


More by this author
Institution:
University of Oxford
Division:
HUMS
Department:
Linguistics Philology and Phonetics Faculty
Research group:
Phonetics Laboratory
Role:
Author
More by this author
Institution:
University of Oxford
Division:
HUMS
Department:
Linguistics Philology and Phonetics Faculty
Research group:
Phonetics Laboratory
Role:
Author
Publication date:
2010-01-01
Language:
English
Keywords:
Subjects:
UUID:
uuid:99e3b72f-2226-4a63-9c45-f8e1263ba762
Local pid:
ora:4614
Deposit date:
2010-12-14

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP