Below is a pdf with some exercises for that i use in my course on inverse problems. Problem 0: Goal understand why it is attractive to use loglikelihoods over likelihoods. Problem 1: An exact model with a single model parameter. This makes easy to plot. Problem 2: A variant of problem 1 with an inexact model. Problem 3: Goal: Show that it is easier to calculate derived quantities such as "expected value" of the posterior when you use mcmc. Problem 4: A problem where it is easy to understand why is the likelihood called a likelihood. Problem 5: A problem where you have to grasp many concepts. Exercises 4 & 5 are probably the most fun. |

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