Presentation Summary

In this presentation, Dr. Mishkin will share her expertise on Bayesian Causal Networks (BCNs), which are a useful tool for complex, multivariate systems in which variable interactions are informative to understanding management and interventions. BCN is also a useful tool for monitoring as updating models is extremely easy once a model is created. BCNs can easily integrate qualitative and quantitative data simultaneously and rely on higher order variance to explain conditional influence and directionality in causal relationships between and among variables. They are frequently referred to as Bayesian Nets or Bayesian Belief Networks. 

Looking to Learn More?

Check out Dr. Mishkin’s course on Bayesian Causal Networks here.

Presenter Bio

Dr. Mishkin is a Geographer and Interdisciplinary Ecologist who is broadly interested in understanding how to understand, predict, and monitor complex human-ecological systems. She focuses primarily on Bayesian modeling and decision science. She is currently active in using Bayesian Causal Networks to understand protected area management in the Monarch Butterfly Biosphere Reserve. The primary goal of her research is to refine methods to predict how particular variables interact in complex systems to provide intervention foci and efficient management monitoring over the short and long term.

Before joining CWS, Dr. Mishkin worked as a faculty member at Unity College and as a Postdoctoral Scholar at the University of Florida and Universidad Nacional Autónoma de México as a CONACYT fellow. She is a lecturer III at the University of Southern Maine and is a Subject Matter Expert for various universities.