AD Model Builder
(Redirected from Automatic Differentiation Model Builder (ADMB))
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An AD Model Builder is an open-source software created by David A. Fournier for nonlinear statistical modelling based on automatic differentiation .
- AKA: ADMB, Automatic Differentiation Model Builder.
- See: Markov chain Monte Carlo, Bayesian Model, Laplace Approximation.
References
2016
- (Wikipedia, 2015) ⇒ https://www.wikiwand.com/en/ADMB Retrieved 2016-07-03
- ADMB or AD Model Builder is a free and open source software suite for non-linear statistical modeling. It was created by David Fournier and now being developed by the ADMB Project, a creation of the non-profit ADMB Foundation. The "AD" in AD Model Builder refers to the automatic differentiation capabilities that come from the AUTODIF Library, a C++ language extension also created by David Fournier, which implements reverse mode automatic differentiation. A related software package, ADMB-RE, provides additional support for modeling random effects.
2012
- (Fournier et al., 2012) ⇒ David A. Fourniera, Hans J. Skaugb, Johnoel Anchetac, James Ianellid, Arni Magnussone, Mark N. Maunderf, Anders Nielseng & John Sibert (2012). “AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optimization Methods and Software", 27(2), 233-249. DOI:10.1080/10556788.2011.597854 [1]
- Many criteria for statistical parameter estimation, such as maximum likelihood, are formulated as a nonlinear optimization problem. Automatic Differentiation Model Builder (ADMB) is a programming framework based on automatic differentiation, aimed at highly nonlinear models with a large number of parameters. The benefits of using AD are computational efficiency and high numerical accuracy, both crucial in many practical problems. We describe the basic components and the underlying philosophy of ADMB, with an emphasis on functionality found in no other statistical software. One example of such a feature is the generic implementation of Laplace approximation of high-dimensional integrals for use in latent variable models.