Conditional Distributions & Conditional Expectation

Learn conditional distributions and prediction through route mixtures, sensor models and random workloads. Prerequisites: joint densities, discrete conditional probability, expectation and variance. Apply the tower property and total-variance decomposition, then investigate residual error, information refinement and conditional laws at continuous observations. Detailed solutions explain assumptions and distinguish average uncertainty reduction from statements about every subgroup.

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Further reading: 5.2.3 Conditional density and independence, 5.1.5 Iterated expectation and total variance.