Causal impact and Bayesian structural time series

Causal impact is a tool for estimating the impact of a one time action. As an example (which we will actually look at the data) consider the BP oil spill in 2010. Let’s say you want to evaluate the impact that this had on BP stocks. Typically with questions like this, we would like to be able to collect multiple samples from a control group and a test group. As this is not possible we would have to try something else. [Read More]

Bayes of our lives: a gentle introduction to Bayesian statistics

Bayesian statistics is an interpretation of statistics. It is used to help explain the frequentist methods and can give much more information. Even if you have never really learnt about Bayesian statistics, I guarantee you have encountered it in some way. Bayes, it’s everywhere In this post, we will only consider a linear model: \(y = \beta x + \epsilon\) where \(\epsilon\) is a standard normal. Suppose we have gathered some data \((Y=\{y_i\}_{i=1}^n, X=\{\{x_{k,i}\}_{k=1}^p\}_{i=1}^n)\), which consist of \(p\) predictors and \(n\) observations, and we wish to fit a linear model. [Read More]