Recent short courses
Hierarchical random effects models using Markov chain Monte Carlo: Analysis of spatio-temporal data
A one day workshop at the 2014 joint Graybill/ENVR Conference on "Modern Statistical Methods for Ecology," hosted by the Department of Statistics at Colorado State University, September 7, 2014.
This is a joint workshop between the "Ecology and Environment," "Bayes Methods," and "Spatial Statistics" working groups of the German Region of the International Biometric Society together with the "Forest Biometrics" unit of the German Association of Forest Research Stations, Freising, Germany, November 6-8, 2013. → Materials
Applied Bayesian spatio-temporal data analysis
National Ecological Observatory Network (NEON) Applied Bayesian Regression Workshop, March 7--8, 2013.
Applied Bayesian regression analysis using R and JAGS
A very very shortcourse offered through MSU's Center for Statistical Training and Consulting (CSTAT), January 25, 2012. → Materials
Bayesian Modeling for Spatial and Spatio-Temporal Data Analysis
The University of Nebraska–Lincoln, Department of Statistics, Lincoln, NE, on October 15-16, 2012.
Bayesian Modeling for Spatial and Spatio-Temporal Data with Applications to Environmental Sciences and Public Health
Frontiers of Statistical Decision Making and Bayesian Analysis, San Antonio, TX, on March 17, 2010.
Hierarchical Modeling and Analysis of Spatial-Temporal Data: Emphasis in Forestry, Ecology, and Environmental Sciences
International Biometric Society Eastern North American Region (ENAR), San Antonio, TX, March 15-18, 2009. → Materials
Forestry/Geography 867, Hierarchical Bayesian Models for Environmental Spatial Data Analysis, Annually Spring Semester (Graduate Course)
Course description: This course explores recent advancements in hierarchical random effects models using Markov chain Monte Carlo (MCMC) methods. The focus is on linear and generalized linear modeling frameworks that accommodate spatial and temporal associations. Lecture and labs offer an applied perspective on model specification, identifiability of parameters, and computational considerations for Bayesian inference from posterior distributions. The lecture series begins with a basic introduction to Bayesian hierarchical linear models and proceed to address several common challenges in environmental data, including missing data and when the number of observations is too large to efficiently fit the desired hierarchical random effects models. The labs blend modeling, computing, and data analysis including a hands-on introduction to the R statistical environment. Special attention is given to exploration and visualization of spatial-temporal data and the practical and accessible implementation of spatial-temporal models.
Forestry/Geography 472, Ecological Monitoring and Data Analysis, Annually Fall Semester
Course description: Design of ecological monitoring systems and analysis of resulting ecological data sets. Monitoring system design, model specification and implementation, and computational considerations from both a design- and model-based perspective. Hands-on introduction to statistical software.
Forestry 408, Forest Resource Management, Annually Fall Semester
Course description: Depending on the type of ownership, foresters must assume varied roles and use different tools to identify and manage for given objectives. Therefore, this course addresses the fundamental decisions foresters make in managing forests for diverse ownership objectives across stands and forests. The focus is on producing timber for revenue, maintaining active management, and sustaining non-timber forest products. The course considers basic tools used in making informed and defensible management decisions. Special attention is given to contemporary topics such as management strategies for biomass production, carbon markets, requirements and challenges of green certification, and promoting non-timber ecosystem services. A portion of the course is also dedicated to learning and applying geospatial tools used in forest assessment and management across landscapes with diverse landuse and ownerships patterns.