Lecture
This lecture covers the generation of Gaussian random vectors, focusing on conditional distributions. The instructor explains how to generate vectors with specific components based on observed values, highlighting the importance of factorizing covariance matrices. Different techniques for generating from conditional distributions are discussed, including correcting vectors generated from unconditional processes. The lecture also delves into Gaussian processes, defining Gaussian processes as collections of random variables with Gaussian distributions. The instructor explains the concept of positive definite covariance functions and how they are essential for defining Gaussian processes. The lecture concludes with a practical example of generating a Brownian bridge as a conditional Gaussian process.