Applying gradient descent – primer / refresher
Every so often a problem arises where it’s appropriate to use gradient descent, and it’s fun (and / or easier) […]
Tag: Statistics
Hidden Markov Model example in r with the depmixS4 package
Recently I developed a solution using a Hidden Markov Model and was quickly asked to explain myself. What are they […]
Bayesian Network Example with the bnlearn Package
Bayesian Networks are probabilistic graphical models and they have some neat features which make them very useful for many problems. […]
Cribbage: Optimal Hand (part 1)
Cribbage is one of my favourite card games and have been playing it ever since I could count. It is […]
Probability of Selecting Matched Pairs and the Hypergeometric Distribution
The problem Consider a case where we have a bag of marbles of size . The bag consists of black […]
PCA vs Autoencoders for Dimensionality Reduction
There are a few ways to reduce the dimensions of large data sets to ensure computational efficiency such as backwards […]
Confidentialise Your Data with the randomNames Package
Sensitive data has it’s restrictions for good reason. Personal data such as names and other identifiable information should be protected. […]
Improve Your Training Set with Unsupervised Learning
On my previous post Advanced Survey Design and Application to Big Data I mentioned unsupervised learning can be used to […]
Advanced Survey Design and Application to Big Data
I like to describe Official statistics as the All Bran of statistics, it’s bland and a bit boring but it […]
Deep Neural Network from Scratch in R
Neural networks evolved in the computer science domain are often the first thing people think of when they hear machine […]
Unsupervised Random Forest Example
A need for unsupervised learning or clustering procedures crop up regularly for problems such as customer behavior segmentation, clustering of […]
Design Matrix for Regression Explained
Recently I was asked about the design matrix (or model matrix) for a regression model and why it is important. […]
Introduction to Outlier Detection
Outlier detection and treatment is an important part of data analysis and one of the first things you should do […]
Singular Value Decomposition: Image Compression
Singular value decomposition (SVD) is a matrix factorisation technique. It is a method to factorise any given matrix into 3 […]
What is the Bayes Factor anyway?
The Bayes Factor is a quantitative measure of much evidence there is for hypothesis A relative to hypothesis B given […]
