Articles by R | All Your Bayes

Quantifying the Expected Value of Information

August 2, 2020 | R | All Your Bayes

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. Deciding whether or not such services are worthwhile is challenging - how do we ...
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Maximum Likelihood Estimation

July 27, 2020 | R | All Your Bayes

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 ...
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Bayesian Logistic Regression with Stan

February 14, 2020 | R | All Your Bayes

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 ...
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Animating Plots

June 3, 2019 | R | All Your Bayes

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. Making Moving Plots ...
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June 3, 2019 | R | All Your Bayes

TLDR Wordclouds can be used to produce a neat summary of text and can readily be produced in R. This is a simple example based on a recent conferene paper. Summarising the content of a conference paper There is an R package dedicated to creating wordclouds, so I’ve started ...
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