Blog
Model Comparison with Hierarchical Models
Comparing the performance of multiple machine learning models using Bayesian Hierarchical models.
Uncertainty in Neural Networks
Using MC Dropout to get probability intervals for neural network predictions.
Entity Embeddings
Creating entity embeddings for categorical predictors using Python.
Neural Networks in R
This post explores how to create a simple neural network to learn a linear function and a non-linear function using both standard R and the Torch library for R.
Functional Programming and Hidden Markov Models
Multi State Models
Tidy Tuesday: The Office
Tidy Tuesday: US Tuition Data
Releasing Harrier League Data
Tidy Tuesday: NHL Goalscorers
Analysing .fit files in R
Forward Mode AD in R
Hamiltonian Monte Carlo in R
Bayesian Linear Regression with Gibbs Sampling in R
Multi-armed Bandits in Scala
Scala and Jupyter Notebook with Almond
Sampling from a distribution with a known CDF
Bayesian Inference using rejection sampling
A Statistical Model for Finishing Positions at the National Cross Country
Efficient Markov chain Monte Carlo in R with Rcpp
Harrier League Cross Country
MCMC with Scala Breeze
An Akka HTTP Client with JSON Parsing
Using Monads for Handling Failures and Exceptions
Seasonal DLM
