Registration for the NIMBLE virtual workshop, January 4-6, 2023

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This form is gathering participant information for the January 2023 workshop to be held virtually 8 am - 1 pm each day (California time, PDT).

Please fill out this form, so that we can gather information about who is attending and tailor the workshop to attendee interests and experience levels. Please then pay for the workshop at https://na.eventscloud.com/714235.

The cost is $125 for regular attendees, $50 for students. If you are a grad student or postdoc (or someone in a similar situation) with no research funding that could cover your cost and the cost would be a hardship for you, you may apply at https://forms.gle/Dxn7vFojKGsbkUzz8 by December 10 for one of a limited number of fee waivers we are offering.

Email *

Name *

Please give your affiliation (university/government agency/company/etc.) and department/unit within your organization. *

Your position *

University faculty/researcher/staff

University postdoc

Graduate student

Government/industry/non-profit researcher

Other:

How likely are you to attend? *

I will definitely attend.

I will likely attend.

I am interested, but not sure yet if I can attend.

I can no longer attend (after initially checking one of the options above).

Other:

How much experience do you have with NIMBLE? *

I've never used NIMBLE, nor have I used WinBUGS or JAGS or Stan.

I've used JAGS or BUGS but not NIMBLE.

I've used Stan but not NIMBLE.

I've set up a model and basic MCMC in NIMBLE.

I've used NIMBLE extensively to fit models but not done any other coding with NIMBLE.

I've written user-defined distributions or functions in NIMBLE or done other basic NIMBLE programming with nimbleFunctions.

I've used nimbleFunctions extensively.

Other:

This question relate to our new automatic differentiation (AD) capabilities in NIMBLE. Please indicate which of the following AD-related topics you are interested in (you can choose more than one).

Hamiltonian Monte Carlo (HMC)

Laplace approximation

Using automatic differentiation to create algorithms in NIMBLE

Using automatic differentiation to get gradients of functions in R (this uses nimbleFunctions)

Other:

Please indicate any relevant additional details about your level of experience with NIMBLE and what topics you are most interested in. *

We may have time to go into more depth on certain topics. Please indicate which potential areas you would be interested in.

Using automatic differentiation in algorithms in NIMBLE

Programming algorithms in NIMBLE

Ecological models

Spatial models and mapping (e.g., disease mapping)

Bayesian non-parametrics

Sequential Monte Carlo (aka particle filtering)

Calling out to external C++ or R code from nimble algorithms and functions

Other:

A copy of your responses will be emailed to the address you provided.

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