Notes

Computational Statistics

1. Basic Markov chain Monte Carlo Method

2. PDMP and MCMC

3. Geometric Ergodicities of Langevin and Barker Algorithms

4. Introduction to Sequential Monte Carlo

Machine Learning

1. Wasserstein Gradient Flow

2. Introduction to Gaussian Processes

3. Introduction to Bayesian Optimisation

4. Introduction to Kernel Stein Discrepancy (with Lanya Yang)

Biostatistics

1. Multiple Testing Problem

2. Response-Adaptive Randomisation (code1) (code2)

Course notes

1. Spectral Theory

2. Probability Theory

Uncategorised

1. Univariate Extreme Value Modelling