Welcome to Gumbi’s documentation!

Getting Started:

• Introduction

Examples:

• The Cars Dataset
  • Simple regression
  • Independent regression for each class in a category
  • Correlated multi-input regression across different classes in a category
  • Multi-output regression
  • Multi-input multi-output regression
  • Multi-dimensional regression
• Simple Regression and Prediction
  • Setup
  • Building and fitting a model
  • Making predictions
  • Visualizing predictions
  • Higher-dimensional predictions and slicing
• Multioutput Regression
  • Setup
  • Train Model
  • Marginal Parameter Predictions
  • Correlated Parameter Predictions
• Bayesian Optimization with the Botorch backend
  • Single-output regression
  • Multi-output regression
  • Multi-input multi-output regression
  • Mixed categorical-continuous regression
  • Multi-input regression
  • Multi-output (Pareto front) optimization with qNEHVI

API:

• gumbi
  • gumbi.aggregation
    • DataSet
    • MetaFrame
    • Standardizer
    • TidyData
    • WideData
  • gumbi.arrays
    • LayeredArray
    • LogitNormal
    • MVUncertainParameterArray
    • MultivariateNormalish
    • ParameterArray
    • UncertainArray
    • UncertainParameterArray
  • gumbi.plotting
    • ParrayPlotter
  • gumbi.regression
    • Regressor
  • gumbi.utils
    • GPyTorchInverseGammaPrior
    • gumbi.utils

Indices and tables

• Index

• Module Index

• Search Page