Substantial advances in Bayesian methods for causal inference have been developed in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who ...

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/

We are looking for volunteers to organize the next StanCons for 2025 (and beyond!) If you are interested in making a proposal (or even just discussing the possibility of making one), please consider ...

YouTube video by Stan