ADMB Workshop; March 9 - 10, 2009
Dr. Stephanie Hampton, Deputy Director of the National Center for Ecological Analysis and
Synthesis (NCEAS), announced plans to host a workshop on ADMB applications to ecology. The workshop will take place March 9 and 10 at NCEAS in Santa Barbara. 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 a response to Dr. Hampton (email@example.com) 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.