If we subdivide the random vector of a Multivariate Normal/Gaussian, what are the marginal of the subvectors? And how is the conditional between the two? Here are the notes: https://raw.githubusercontent.com/Ceyron/machine-learning-and-simulation/main/english/essential_pmf_pdf/multivariate_normal_marginal_and_conditional.pdf
The Multivariate Normal allows for many analytical computations that are infeasible with other (joint) distributions.
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Timestamps:
00:00 Introduction
01:25 What partitioning means for the parameters
02:39 Marginal
04:06 Marginal: Visualization for Bivariate Normal
08:26 Conditional: Bayes' Theorem
09:43 Conditional: Idea for Derivation
10:02 Conditional: Recap Multivariate Normal
10:47 Conditional: Precision Matrix
11:45 Conditional: Inserting Subdivision
19:46 Conditional: Ignoring terms
22:32 Conditional: Completing the Square
27:43 Conditional: Discussion on mu
28:08 Conditional: Preliminary Solution
20:33 Conditional: Schur Complement
37:28 Summary
40:19 Outro