Non-Linear Searches#

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

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

Examples / Tutorials:

Nested Samplers#

`DynestyDynamic`	A Dynesty non-linear search, using a dynamically changing number of live points.
`DynestyStatic`	A Dynesty NonLinearSearch using a static number of live points.

MCMC#

`Emcee`	An Emcee non-linear search.
`Zeus`	A Zeus non-linear search.

Maximum Likelihood Estimators#

`BFGS`	Abstract wrapper for the BFGS and L-BFGS scipy non-linear searches.
`LBFGS`	Abstract wrapper for the BFGS and L-BFGS scipy non-linear searches.

There are also a number of tools which are used to customize the behaviour of non-linear searches in PyAutoFit, including directory output structure, parameter sample initialization and MCMC auto correlation analysis.

Tools#

`DirectoryPaths`	Manages the path structure for NonLinearSearch output, for analyses both not using and using the search API.
`DatabasePaths`	Manages the path structure for NonLinearSearch output, for analyses both not using and using the search API.
`Result`	The result of a non-linear search.
`InitializerBall`	The Initializer creates the initial set of samples in non-linear parameter space that can be passed into a NonLinearSearch to define where to begin sampling.
`InitializerPrior`	The Initializer creates the initial set of samples in non-linear parameter space that can be passed into a NonLinearSearch to define where to begin sampling.
`AutoCorrelationsSettings`	Class for performing and customizing AutoCorrelation calculations, which are used:

PyAutoFit can perform a parallelized grid-search of non-linear searches, where a subset of parameters in the model are fitted in over a discrete grid.

Examples / Tutorials:

GridSearch#

`SearchGridSearch`
`GridSearchResult`	The sample of a grid search.