Lens Modeling#

Analysis#

The Analysis objects define the log_likelihood_function of how a lens model is fitted to a dataset.

It acts as an interface between the data, model and the non-linear search.

`AnalysisImaging`	Fits a lens model to a dataset via a non-linear search.
`AnalysisInterferometer`	Analysis classes are used by PyAutoFit to fit a model to a dataset via a non-linear search.

Non-linear Searches#

A non-linear search is an algorithm which fits a model to data.

PyAutoGalaxy currently supports three types of non-linear search algorithms: nested samplers, Markov Chain Monte Carlo (MCMC) and Maximum Likelihood Estimaotrs (MLE).

`Nautilus`	A Nautilus non-linear search.
`LBFGS`	Abstract wrapper for the BFGS and L-BFGS scipy non-linear searches.
`BFGS`	Abstract wrapper for the BFGS and L-BFGS scipy non-linear searches.
`DynestyDynamic`	A Dynesty non-linear search, using a dynamically changing number of live points.
`Emcee`	An Emcee non-linear search.
`PySwarmsLocal`	A PySwarms Particle Swarm MLE global non-linear search.
`PySwarmsGlobal`	A PySwarms Particle Swarm MLE global non-linear search.

Priors#

The priors of parameters of every component of a model, which is fitted to data, are customized using Prior objects.

`UniformPrior`	A prior with a uniform distribution, defined between a lower limit and upper limit.
`GaussianPrior`	A prior with a uniform distribution, defined between a lower limit and upper limit.
`LogUniformPrior`	A prior with a log base 10 uniform distribution, defined between a lower limit and upper limit.
`LogGaussianPrior`	A prior for a variable whose logarithm is gaussian distributed.

Adapt#