Overview of initial design strategies for Bayesian optimisation. Implementations in R.
Overview of some functions for testing and benchmarking Bayesian optimisation. Implementations in R.
Surrogate model alternatives to Gaussian processes for Bayesian optimisation. Implementations in R.
A comprehensive overview of acquisition functions for Gaussian processes and Bayesian optimisation. Implementation in R.
A comprehensive overview of kernels, or covariance functions, for Gaussian processes and Bayesian optimisation. Implementation in R.
Developing a bespoke Bayesian model for dose-response screening assays with batch effects
All the basic components of Bayesian optimisation introduced and implemented in R
Developing a bespoke Bayesian model for fitting the Hill equation in biochemical assays
Exploring a dataset of Olympic athletes over time using Bayesian data analysis and the Tidyverse
Developing a bespoke Bayesian model for high throughput screening assays