This video introduces the notions of independence, conditional densities, and Bayes's theorem. The use of independence in signal processing applications such as Blind Source Separation is introduced, although this will be expanded in future videos on Statistical Signal Processing. Analytical tests for independence given the probability density function is considered for a couple of examples, including deriving the joint density for independent Gaussian random variables. Conditional densities are then introduced, and Bayes theorem for solving inverse problems is developed from this. The final section of the video then considers in detail the problem of estimating a parameter from a noisy observation.