Computing
Fun!!
Introduction to computing and analytical review
The objectives of this laboratory are:
- To familiarize students with the SAS computing
environment
- To review the basic steps of
model fitting including binary coding of categorical predictors, data
transformation, goodness-of-fit
- To
review the interpretation and presentation of generalized linear model
parameter estimates
Computing review
Information theoretic
approach to model selection and inference
The objectives of this laboratory are:
- To familiarize students with information
theoretic approaches
- To examine the relationships between model
selection, accuracy, and precision via computer intensive approaches
- To examine the effects of parameter uncertainty
on inference
AIC modeling exercise
Evaluating Model Performance
The objectives of this laboratory are:
- To familiarize students with the concepts of model prediction accuracy
- To introduce students to techniques for estimating
model accuracy
Estimating model performance and prediction accuracy
Neutral
models
The objectives of this laboratory are:
- To familiarize students with randomization
- To introduce students to standard techniques for examining
neutral models
- To introduce students to simulation modeling
Neutral modeling exercise
Landscape
and classification modeling
The objectives of this laboratory are:
- To familiarize students with parametric and
non-parametric approaches to modeling categorical data, such as species presence
-
To introduce students to techniques for quantifying landscape influences on
species distribution and status
- To examine the accuracy and precision of
the various models
Landscape
modeling exercises
Multi-level (hierarchical)
and repeated measures models for ecological inference
The objectives of this laboratory are:
- To familiarize students with
the detection of spatial and temporal dependencies in data
- To introduce students to techniques for
modeling continuous and discrete data at multiple spatial and temporal
scales
- To examine techniques for model
selection and evaluating out of sample performance
Hierarchical
modeling exercises
Meta-analysis and modeling
The objectives of this laboratory are:
- To examine the type of data amenable to meta
analysis
- To introduce students to Monte
Carlo Markov Chain techniques for hierarchical modeling
-
To examine the various methods
for model selection
Meta-analysis exercises
(Meta)
Population viability measurement and modeling
The objectives of this laboratory are:
- To familiarize students with different approaches to
estimating and modeling population viability
- To introduce students to techniques for
estimating patch occupancy
- To examine the factors influencing the
relative accuracy of (meta) population viability estimates
PVA modeling exercises
Bayesian belief network modeling
The objectives of this laboratory are:
- To familiarize students with Bayes
(probabilistic) networks
- To introduce students to techniques for
parameterizing networks and performing Bayesian updating
- To examine the sensitivity of Bayesian belief network
models
Bayesian belief network
modeling exercises
Decision
modeling and analysis
The objectives of this laboratory are:
- To familiarize students with decision models
- To introduce students to techniques for
building and parameterizing models
- To introduce students to the concept of
multi-model inference
- To demonstrate calculation of the value of
information and imperfect information
Decision
modeling exercises