A practical guide to the probability density approximation (PDA) with improved implementation and error characterization

DOI10.1016/j.jmp.2015.08.006zbMath1359.62501OpenAlexW1711953686MaRDI QIDQ901231

William R. Holmes

Publication date: 23 December 2015

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmp.2015.08.006

zbMATH Keywords

Markov chain Monte Carloapproximate likelihoodkernel density estimatelinear ballistic accumulator modelnonparametric approximate Bayesian computation

Mathematics Subject Classification ID

Density estimation (62G07) Bayesian inference (62F15) Mathematical psychology (91E99) Applications of statistics to psychology (62P15)

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Uses Software

• WinBUGS
• JAGS
• BayesDA
• abc
• AS 176

Cites Work

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• A tutorial on approximate Bayesian computation
• Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
• Sequential Monte Carlo Samplers
• Algorithm AS 176: Kernel Density Estimation Using the Fast Fourier Transform
• A NONPARAMETRIC SIMULATED MAXIMUM LIKELIHOOD ESTIMATION METHOD
• The Identity of Weak and Strong Extensions of Differential Operators

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