Lecture slides and jupyter notebooks for learning and experimenting with probabilistic models. Includes lectures on:
- Probabilistic programming in pymc3
- Fitting a gaussian
- Linear Regression
- Changepoint modelling
- Poisson changepoint
- Bernoulli Changepoint
- Gaussian changepoint for both mean and variance
- Advanced example : Multi-changepoint model with mixture emissions for repeated timeseries
- Gaussian Mixture Models and Hidden Markov Models
- Exercise on clustering using GMMs
- More on Bayesian Changepoint Modelling