Welcome to revrand’s documentation

Contents:

• Quickstart Guide
  • Bayesian Linear Regression Example
  • Bayesian Generalized Linear Model Example
  • Large-scale Learning with Stochastic Gradients
  • Feature Composition Framework
  • References
• Installation
  • Dependencies
• Basis Functions
  • revrand.basis_functions.Basis
  • revrand.basis_functions.BiasBasis
  • revrand.basis_functions.LinearBasis
  • revrand.basis_functions.PolynomialBasis
  • revrand.basis_functions.RadialBasis
  • revrand.basis_functions.SigmoidalBasis
  • revrand.basis_functions.RandomRBF
  • revrand.basis_functions.RandomLaplace
  • revrand.basis_functions.RandomCauchy
  • revrand.basis_functions.RandomMatern32
  • revrand.basis_functions.RandomMatern52
  • revrand.basis_functions.OrthogonalRBF
  • revrand.basis_functions.FastFoodRBF
  • revrand.basis_functions.FastFoodGM
• Standard Linear Model
• Generalized Linear Model
• Likelihood Classes
  • revrand.likelihoods.Bernoulli
  • revrand.likelihoods.Binomial
  • revrand.likelihoods.Gaussian
  • revrand.likelihoods.Poisson
• Bound and Parameter Types
• Validation Metrics
• Optimization
  • Optimizer Decorators
  • Stochastic Gradients
• Utilities
  • General Utilities
  • Dataset Utilities
  • Decorators Utilities
  • Random Generators
• Math Utilities
  • Linear Algebra
  • Special Functions
• Demos
  • Regression
  • Classification
  • Stochastic Gradients
  • Random Kernel Approximation
  • References
• Developer’s Guide
  • Testing
  • Documentation

Indices and tables

• Index
• Module Index
• Search Page