Introduction | Example: Simple Linear Regression | Exercises | Data exploration | Running the Gibbs Sampler | Reducing the autocorrelation by mean-centering the covariate | INLA | Example: Fake News | Example: Emergency Room Complaints | Fitting Bayesian Poisson Regression Models | Bayesian Hierarchical Modelling | Linear Mixed Models | Multilevel Modelling | Generalised Linear Mixed Models | Poisson regression

Fitting a Bayesian polynomial model | Introduction | Fitting a (frequentist) quadratic regression model | Fitting a Bayesian quadratic regression model | Time to individual work

Software for Bayesian Statistical Analysis | BayesX | Other Bayesian Software | Bayesian Logistic Regression | Model Formulation | Example: Fake News | Fitting Bayesian Logistic Regression Models | Model Fitting | Exercises | Bayesian Poisson Regression | Example: Emergency Room Complaints | Fitting Bayesian Poisson Regression Models

Introduction | Bayesian inference for the expected number of u's in a page of A Game of Thrones | The experiment: Number of u's in a page of A Game of Thrones | The sampling model is approximately Poisson | A prior distribution for $\lambda$ | The likelihood function of $\lambda$ | The posterior distribution of $\lambda$ | The posterior predictive distribution for the number of u's in a new page of A Game of Thrones | Time to individual work

How tall the VIBASS' participants are? | Bayesian inference for the mean height of the women VIBASS' participants. The variance of the sampling Normal model is known. | The data | The sampling model is approximately Normal | A prior distribution for $\mu$ | The likelihood function of $\mu$ | The posterior distribution of $\mu$ | The posterior predictive distribution for the height of a new VIBASS participant | Bayesian inference for the mean height of the women VIBASS participants. The variance of the sampling Normal model is unknown. | A prior distribution for $(\mu,, \sigma^2)$ | The likelihood function of $(\mu,, \sigma^2)$ | The posterior distribution of $(\mu,, \sigma^2)$ | Time to individual work

Simulation-based inference in a Bayesian quadratic model | Introduction | Posterior distribution for the model parameters | Analytical inference | Simulation-based inference | Predictive inference | Time to individual work

Introducing M$&$M's | Bayesian inference for the proportion of red M$&$M's | The experiment: Counting red M$&$M's | The sampling model is Binomial | A prior distribution for $\theta$ | The likelihood function of $\theta$ | The posterior distribution of $\theta$ | The posterior predictive distribution for the results of a new experiment | Time for individual work

Bayesian Hierarchical Modelling | Linear Mixed Models | Multilevel Modelling | Exercises | Generalised Linear Mixed Models | Poisson regression | Further Extensions | Final Exercises (Optional!!)

Introduction | Importance Sampling | The Metropolis-Hastings Algorithm | Example: Poisson-Gamma Model | Importance sampling | Metropolis-Hastings | Exercises | Performance of the proposal distribution | Changing the proposal distribution - Importance Sampling | Changing the prior distribution - Metropolis-Hastings | Gibbs Sampling | Example: Simple Linear Regression | Data exploration | Running the Gibbs Sampler | Reducing the autocorrelation by mean-centering the covariate