============ Basics of Bayesian inference and parameter estimation ============ To use :code:`redback` we assume some level of familiarity with Bayesian inference and model fitting. However, if this is not the case, :code:`bilby` provides a basic demonstration of Bayesian inference and how it is implemented in :code:`bilby`. An example for a basic problem of fitting a line is available in the :code:`bilby` `documentation `_. :code:`redback` workflow ------------------------- In :code:`redback`, we make this process homogenous specifically for fitting electromagnetic transients. The :code:`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 :code:`bilby` - Fit model! - 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 :code:`redback`. - Modify the physics of a transient model by passing in different class constructors (dependency injections). - Place constraints on priors if necessary. - Joint analysis. - Reweighting. - Hierarchical inference.