A review of entropy, conditional entropy, and mutual information, with the basic proofs. I then set up how I plan to use mutual information to relate spatial mappings between cortical regions to their temporal coupling.
Working through the definition of convergence in probability from Casella and Berger, using Chebyshev's inequality to show the weak law of large numbers for the sample mean and variance, and simulating normal samples to watch both estimates converge.
A sum of independent Poisson variables is Poisson with the summed rate. Conditioned on that sum, the individual counts follow a multinomial distribution.
Deriving the marginal and conditional distributions of a partitioned multivariate normal.