The course is full.

- Syllabus and 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 and deviance on the Normal example.
- Poisson distribution: The didactic simplicity of Poisson and its likelihood. Likelihood maximization. Probability mass function.
- The Bayesian way and the principle of MCMC sampling.
- Simplest model in JAGS. Estimating lambda parameter of simple count data using MCMC.
- 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 and part 2: Extracting credible and prediction intervals.
- ANOVA part 1 and part 2: Fixed vs. random effects, the effect of small sample, shrinkage.

**DAY 3**

- Simple autoregressive (AR) and generalized additive models (GAM), time series analysis.
- Site occupancy model: logistic regression, imperfect observation, latent variables.
- Useful probability distributions (binomial, beta, gamma, multivariate normal, negative binomial).
- Publishing Bayesian papers.
- Concluding remarks.

**RELATED TOPICS AND POSTS**

- Survival analysis.
- Spatial model using GAM splines in JAGS.
- Previous year's lesson on time series analysis using the lynx dataset.
- Poisson regression using GLM, maximum likelihood and JAGS.
- Tailoring custom distributions.
- Bayesian Principal Components Analysis (bPCA)

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