https://mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 ------------------------------------------------------------ Cristina Barber, Andrii Zaiats, Cara Applestein, Trevor Caughlin, Boise State University Spatial models for plant neighborhood dynamics in Stan ------------------------------------------------------------ Spatial interactions between neighboring plants are foundational to population and community ecology. Neighbor interactions include both positive (facilitation) and negative (competition) effects. The strength of interaction between neighboring plants decays with distance and is often modeled using non-linear functions that take into account a target plant and all its neighbors, resulting in large matrices that represent pairwise interactions. The structural complexity of neighbor interactions and the large size of neighborhood data structures results in long run times and convergence problems. In this talk, we demonstrate how Stan’s segment function can speed computation on sparse matrices of pairwise neighbors in plant-plant interaction models. In addition, we present solutions to common problems of fitting neighborhood models with hierarchical effects, including a comparison of centered vs. non-centered parameterizations. We conclude that the flexibility of the Stan programming language presents novel opportunities to fit classic spatial models in ecology. ​ Documentation: https://cristinabarber.github.io/StanCon2020_Neighbor_interaction_models/

https://mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 ------------------------------------------------------------ Cristina Barber, Andrii Zaiats, Cara Applestein, Trevor Caughlin, Boise State University Spatial models for plant neighborhood dynamics in Stan ------------------------------------------------------------ Spatial interactions between neighboring plants are foundational to population and community ecology. Neighbor interactions include both positive (facilitation) and negative (competition) effects. The strength of interaction between neighboring plants decays with distance and is often modeled using non-linear functions that take into account a target plant and all its neighbors, resulting in large matrices that represent pairwise interactions. The structural complexity of neighbor interactions and the large size of neighborhood data structures results in long run times and convergence problems. In this talk, we demonstrate how Stan’s segment function can speed computation on sparse matrices of pairwise neighbors in plant-plant interaction models. In addition, we present solutions to common problems of fitting neighborhood models with hierarchical effects, including a comparison of centered vs. non-centered parameterizations. We conclude that the flexibility of the Stan programming language presents novel opportunities to fit classic spatial models in ecology. ​ Documentation: https://cristinabarber.github.io/StanCon2020_Neighbor_interaction_models/

https://mc-stan.org The Stan Conference 2020. August 13, 2020. #stancon2020 ------------------------------------------------------------ Matthew Kay, University of Michigan Building effective uncertainty visualizations with Stan/brms, tidybayes, and ggdist ------------------------------------------------------------ Bayesian modeling and effective uncertainty visualization are a natural pair: Bayesian modeling techniques produce samples from joint probability distributions that describe the uncertainty in estimates and predictions, and a growing body of research suggests that sampling-based visualizations of uncertainty can lead to better estimates and better decisions from users. In this talk I will tour a variety of modern uncertainty visualization techniques, discussing systematic principles for matching uncertainty encodings to the communication and decision goals of an uncertainty visualization, grounded in perceptual and cognitive aspects of uncertainty visualization understanding. Along the way, I will demonstrate programming techniques for easily constructing complex uncertainty visualizations using Stan/brms with the tidybayes (http://mjskay.github.io/tidybayes/) and ggdist (https://mjskay.github.io/ggdist/) R packages, which are designed specifically for creating uncertainty visualizations. Examples will be drawn from existing vignettes (e.g. http://mjskay.github.io/tidybayes/articles/tidy-brms.html and https://mjskay.github.io/ggdist/articles/slabinterval.html), my repository of uncertainty visualization examples (https://github.com/mjskay/uncertainty-examples), and the uncertainty visualization literature (e.g. medical risk communication, hurricane path prediction, and real-time transit arrival prediction). The goal of the talk will be to give the audience some grounding in the basic principles of effective uncertainty visualization design, and then demonstrate how these principles can be applied to visualizing uncertainty from Stan models using APIs expressly designed for that purpose. Documentation: http://mjskay.github.io/tidybayes/ https://mjskay.github.io/ggdist/ https://github.com/mjskay/uncertainty-examples