TLDR We sometimes have the option to purchase additional information (like paying for an experiment to be performed, or for a report from an expert consultant) to help us in problems of decision making under uncertainty.
TLDR Maximum Likelihood Estimation (MLE) is one method of inferring model parameters. This post aims to give an intuitive explanation of MLE, discussing why it is so useful (simplicity and availability in software) as well as where it is limited (point estimates are not as informative as Bayesian estimates, which are also shown for comparison).
TLDR Logistic regression is a popular machine learning model. One application of it in an engineering context is quantifying the effectiveness of inspection technologies at detecting damage. This post describes the additional information provided by a Bayesian application of logistic regression (and how it can be implemented using the Stan probabilistic programming language).
TLDR There are many instances where it may be useful to animate graphical representations of data, perhaps to add an additional dimension to a plot. The below example builds a cumulative map of car accidents in the UK using some of the functionality of the gganimate package.