Welcome to Random Walks! This is an archive of explorations, in the convex hull of Inference, Probability, Optimisation and Physics. I am putting this together to keep track of the things I study.
- Notes on the book An Introduction to Probability by Grimmett and Welsh.
- Approximate inference for Gaussian Processes.
- Stochastic processes and stochastic differential equations.
- Bayesian Methods for Adaptive Models: A reproduction of David MacKay's PhD thesis.
- The Neural Process family: Conditional Neural Processes (CNPs), Neural Processes (NPs) and Attentive Neural Processes (ANPs).
- Probabilistic methods for Reinforcement Learning, like PILCO.
[19/04/2021] Notes and demo on Stein Variational Gradient Descent.
[02/04/2021] Notes and demo on Random Fourier Features.
[20/03/2021] Notes and demo on Interacting particle solutions of FPK equations through GLD estimation.
[10/03/2021] Short notes on the paper: Estimation of non-normalized statistical models by score matching.
[13/02/2021] Notes and a demo for the paper: Gaussian process approximations of stochastic differential equations.
[01/02/2021] Additional material on Conjugate Gradients.
[21/01/2021] Notes and demo on Natural Cubic Splinces.
[14/01/2021] Notes and demo on Adaptive Rejection Sampling.
[12/10/2020] Short notes on Conjugate Gradients
[10/09/2020] Short notes on the last three chapters of Grimmett and Welsh, An Introduction to Probability.
[06/09/2020] Short notes on the first nine chapters (out of 12) of Grimmett and Welsh, An Introduction to Probability.
[25/07/2020] A reproduction of the paper: Higham, An algorithmic Introduction to Numerical Simulation of SDEs.