Welcome to bayesdawn’s documentation!

BayesDawn stands for Bayesian Data Augmentation for Waves and Noise. It implements an iterative Bayesian augmentation method to handle data gaps in gravitational-wave data analysis, as described in this paper: https://arxiv.org/abs/1907.04747.

• Quick start: using Bayesdawn on a simple example
  • 0. Import relevant modules
  • 1. Generate example data
  • 2. Define your PSD function and embed it in a class
  • Check that the PSD function is consistent with the data
  • 3. Instantiate the imputation class
  • 4. Perform missing data imputation
  • 5. Comparing time series
• Imputation methods
  • Available methods
  • Method use
• bayesdawn
  • bayesdawn package

Installation

BayesDawn can be installed by unzipping the source code in one directory and using this command:

sudo python setup.py install

Indices and tables

• Index

• Module Index

• Search Page