Welcome to bilby’s documentation!

Bilby

Bilby: a user-friendly Bayesian inference library.

The aim of bilby is to provide user friendly interface to perform parameter estimation. It is primarily designed and built for inference of compact binary coalescence events in interferometric data, but it can also be used for more general problems.

The code, and many examples are hosted at https://git.ligo.org/lscsoft/bilby. For installation instructions see https://lscsoft.docs.ligo.org/bilby/installation.html.

Contents:

• Installation
  • Install bilby from source
    • Installing optional requirements
  • Installing python
  • Installing lalsuite
  • Common problems: multiple bilby versions
• Code overview
• Examples
• Basics of parameter estimation
  • The maths
  • The code
  • Final thoughts
• Priors
  • Defining priors
  • Default priors
  • The bilby prior dictionary
  • Available prior classes
  • Multivariate Gaussian prior
  • Defining your own prior
  • Prior Constraints
    • Simple example
    • Gravitational wave priors
  • Prior Examples
    • Aligned spins waveform with tides off
    • Aligned spins waveform with tides on
    • Precessing spins waveform with tides off
    • Precessing spins waveform with tides on
  • Priors using a Jupyter notebook
• Likelihood
  • The simplest likelihood
  • General likelihood for fitting a function \(y(x)\) to some data with known noise
  • General likelihood for fitting a function \(y(x)\) to some data with unknown noise
  • Common likelihood functions
  • Empty likelihood for subclassing
• Sampling
  • Switching between samplers
  • Nested Samplers
    • Dynesty
    • Nestle
    • CPNest
    • PyMultinest
    • PyPolyChord
  • MCMC samplers
    • Emcee
    • PTEmcee
    • pymc3
  • Installing samplers
    • pip-installable samplers
    • Installing PyPolyChord
    • Installing pymultinest
  • Adding new samplers to bilby
    • A base Sampler class
    • A Nested Sampler class
    • A MCMC Sampler class
• Dynesty Guide
  • Overview of dynesty
  • Bilby-specific implementation details
  • Understanding the output
• Bilby output
  • The result file
  • Reading in a result file
  • Accessing samples directly
  • Saving to ASCII text
  • Visualising the results
• Compact binary coalescence parameter estimation
• Transient gravitational wave data I/O
  • What is used by the likelihood?
  • Making an Interferometer
  • Setting the strain data
    • Setting the strain data using gwpy
    • Setting the strain data directly
    • Setting the strain data to be Gaussian noise
    • Setting the strain data to be zero noise
  • Injecting a signal
• Transient gravitational wave likelihood
• Transient Graviatiaonal wave priors
• Transient gravitational wave parameter conversions
• Transient gravitational wave reference information
  • Detector information
  • Constants
• Writing documentation
  • Writing the basics
  • Checking the results
  • Pushing your changes to master
  • Using reStructured text
  • Sphinx autodoc
• Hyper-parameters
  • Setting up the problem
  • Understanding the population using hyperparameters
• Using Containers
  • Running a bilby script with Singularity