Guides

• Introduction to Bayesian Optimization (BO)
  • Gaussian Process
  • Optimizing the hyper-parameters of a Gaussian process
  • Kernel function
  • Acquisition function
• Limbo-specific concepts
  • Mean function
  • State-based optimization
• Using Limbo as an environment for scientific experiments
  • What is a Limbo experiment?
  • How to quickly create a new experiment?
  • How to add / compile your experiment?
  • How to submit jobs with limbo on clusters?
  • Variants
• Parameters
  • Default parameters
  • Dynamic parameters
  • Arrays
  • Multiple sets of parameters