Basics of Bayesian inference and parameter estimation
redback we assume some level of familiarity with Bayesian inference and model fitting.
However, if this is not the case,
bilby provides a basic demonstration of Bayesian inference and
how it is implemented in
An example for a basic problem of fitting a line is available in the
redback, we make this process homogenous specifically for fitting electromagnetic transients. The
redback workflow for fitting is:
Download the data from a public catalog, or provide your own data. Or simulate it.
Load the data into a homogenous transient object, which does the necessary processing and provides simple way to plot data.
The user then specifies a model (either already implemented in redback or their own function).
Write a prior or use the default priors.
Specify a sampler and sampler settings as in
The fit returns a homogenous result object, which can be used for further diagnostics, and provides a simple way to plot the fit.
More advanced fitting functionality
The likelihood is by default set by the type of transient/data used, the more advanced users can provide their own or use more complicated likelihoods implemented in
Modify the physics of a transient model by passing in different class constructors.
Place constraints on priors if necessary.