[ADMB Users] AD Model Builder Workshop 9 & 10 March 2009, at NCEAS

[JohnS]

On 9 and 10 March 2009, the National Center for Ecological Analysis
and Synthesis (NCEAS) will host a workshop in the use of AD Model
Builder (admb-project.org), with attention to its applications that
are general to ecology. AD Model Builder is a well known modeling
package in the fisheries biology community, with applications that are
general to ecology and other sciences; NCEAS and the ADMB Foundation
together have recently purchased this package in order to make it free
and open source, and to make it accessible to a broader community of
scientists. It is now free to download.

Several seats are available in the March workshop, and the workshop
itself is free of cost, but we can not pay your travel expenses. We
can help to secure lodging within walking distance that is usually
below standard rates for Santa Barbara (e.g., $120/night or less). If
you are interested in attending, please send me a response
(hampton at nceas.ucsb.edu) by 16 Feb 2009. Please include a CV and a
short statement about why you would like to attend - in the event that
we receive more interest than we can accommodate, we will select
participants who represent a breadth of disciplines.

A description of the 2-day ADMB workshop follows. A 1-day workshop
will also be offered at the ESA meeting this summer.

*AD Model Builder: a Free Tool for Parameter Estimation of Complex
Nonlinear Statistical Models
*Instructors: Mark Maunder & Anders Nielsen

This mini-course targets quantitative ecologists, and students who
need to handle complex nonlinear statistical models (both frequentist
and Bayesian). AD Model Builder (admb-project.org) is a highly
efficient freely available software for implementing non-linear
statistical models. The main reasons for preferring AD Model builder
are: 1) Flexibility. The user is free to define any desired model, and
not limited to choose between a set of predefined models. 2) Speed.
Automatic differentiation can make the difference between waiting
hours and seconds for a converging model fit. 3) Precision. Automatic
differentiation calculates the derivatives as accurately as if the
analytical derivatives were implemented. 4) Quantification of
uncertainties. With almost no extra effort AD Model builder produces
several different estimates of the uncertainties of model parameters
and selected derived quantities.

A beginners’ course in ADMB likely will include: 1) An overview of
ADMB. 2) A refresher on model development and likelihood based
inference. 3) Installing and set up the software. 3) A use case. 4)
Options for importing data (the simple and the more exotic). 5)
Definition of model parameters (limits, phases, and some tricks). 6)
Programming the likelihood function. 7) Specification and formatting
of output. 8) Debugging, memory management, and other important
implementation issues. 9) Estimation uncertainties (delta, profile,
and MCMC methods). 10) Random effects models in AD Model Builder.

The actual contents of the course will be customized to fit the
audience. The form will be a mixture between lectures and hands on
exercises.

Stephanie E. Hampton
Deputy Director
National Center for Ecological Analysis & Synthesis
University of California, Santa Barbara
735 State St., Suite 300
Santa Barbara, CA 93101-3351, USA
http://www.nceas.ucsb.edu
hampton at nceas.ucsb.edu
Tel (805) 892-2505
Fax (805) 892-2510