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]

Analysis of calving of JH Dorrington Farm Part II

This is the second part of the analysis for the data from JH Dorrington Farm. You might want to read the first part before reading this one. Before we put on our science hats, let us make an outline for what we will do. Previously we split the data into training and test sets 80/20. We will fit all of our models and calibrate them on the training set. Decisions about keeping/dropping predictors, transforming predictors and which model to chose will be left to the test set. [Read More]

Analysis of calving of JH Dorrington Farm Part I

Here I will analyse a real life problem. My friend Chris at JH Dorrington Farm has kindly provided me with the data and allowed me to make this post. This will be several parts as I explore the data and try to fit various models. I’m going to stop milking this introduction and get right to it. My friend Chris has been collecting various forms of data about his cows. [Read More]