Getting started

• Getting started with PyMC3
  • Abstract
  • Introduction
  • Installation
  • A Motivating Example: Linear Regression
    • Generating data
    • Model Specification
    • Model fitting
      • Maximum a posteriori methods
      • Sampling methods
      • Gradient-based sampling methods
    • Posterior analysis
  • Case study 1: Stochastic volatility
    • The Model
    • The Data
    • Model Specification
    • Fitting
  • Case study 2: Coal mining disasters
  • Arbitrary deterministics
  • Arbitrary distributions
  • Generalized Linear Models
  • Backends
  • Discussion
  • References
• API quickstart
  • 1. Model creation
  • 2. Probability Distributions
    • Unobserved Random Variables
    • Observed Random Variables
    • Deterministic transforms
    • Automatic transforms of bounded RVs
    • Lists of RVs / higher-dimensional RVs
    • Initialization with test_values
  • 3. Inference
    • 3.1 Sampling
    • 3.2 Analyze sampling results
    • 3.3 Variational inference
  • 4. Posterior Predictive Sampling
  • 4.1 Predicting on hold-out data
• Variational API quickstart
  • Basic setup
  • Tracking parameters
  • Replacements
    • basic usage
    • converting trace to Approximation
  • Multilabel logistic regression
    • Applying replacements in practice
  • Minibatches
    • Data
    • no minibatch inference
    • minibatch inference