Introduction to maximum likelihood and Bayesian statistics for ecologists (1-3 March 2017, iDiv)

By | February 27, 2017

The course is full. Here is syllabus with instructions. Complete raw codes (Markdown and R) and materials see the course's GitHub repository.

DAY 1

Introduction: Course contents, pros and cons of Bayes, necessary skills.
Normal distribution: Introducing likelihood on the Normal example.
Poisson distribution: Likelihood maximization. Probability mass function. AIC and deviance.
The Bayesian way and the principle of MCMC sampling.
Simplest model in JAGS: Estimating lambda parameter of a simple Poisson model.
Bayesian resources: Overview of software, books and on-line resources.

DAY 2

t-test: First model with 'effects', hypotheses testing, derived variables.
Linear regression part 1: Traditional GLM, MLE, and manual estimation.
Linear regression part 2: Bayesian version in JAGS, credible and prediction intervals.
ANOVA part 1: Model definition.
ANOVA part 2: Fixed vs. random-effect. Effect of small sample, shrinkage.

DAY 3

Site occupancy model: logistic regression, imperfect observation, latent variables.
Useful probability distributions (binomial, beta, gamma, multivariate normal, negative binomial).
Publishing Bayesian papers.
Concluding remarks.


LITERATURE

Introductory books

Thicker or more technical reading

Ecological models

Manuals & software

  • Plummer (2012) JAGS Version 3.3.0 user manual.
  • Stan Modelling Language -- User's guide and reference manual (2016)
  • OpenBUGS documentation.
  • CRAN Task View on Bayesian Inferenec

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