User’s Guide

• Simple Estimation Functions
  • Related Examples
• Representing Probability Distributions
  • Introduction
  • Sampling Pre-made Distributions
  • Combining Distributions
  • Making Custom Distributions
• Designing and Using Models
  • Introduction
  • Using Models and Simulations
  • Implementing Custom Simulators and Models
  • Adding Functionality to Models with Other Models
• Sequential Monte Carlo
  • Introduction
  • Using SMCUpdater
  • Advanced Usage
• Experiment Design Heuristics
  • Using Heuristics in Updater Loops
  • Changing Heuristic Parameters
• Randomized Benchmarking
  • Introduction
  • Zeroth-Order Model
• Quantum Tomography
  • Introduction
  • Bases
  • Built-in Distributions
  • Using TomographyModel
  • Built-in Heuristics
• Learning Time-Dependent Models
  • Time-Dependent Parameters
  • Random Walk Models
  • Specifying Custom Time-Step Updates
• Performance and Robustness Testing
  • Introduction
  • Testing Estimation Performance
  • Robustness Testing
• Parallel Execution of Models
  • Introduction
  • Distributed Computation with IPython
  • GPGPU-based Likelihood Computation with PyOpenCL
• Interoperability
  • Introduction
  • MATLAB Interoperability
  • Julia Interoperability