The Maxlike sampler

Find the maximum likelihood using various methods in scipy

Name: Maxlike

Version: 1.0

Author(s): SciPy developers

URL: http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.optimize.minimize.html

Cite:

Parallel: serial

This sampler attempts to find the single point in parameter space with the highest likelihood. It wraps a variety of samplers from the scipy.minimize package.

These methods are all iterative and local, not global, so they can only find the nearest local maximum likelihood point from the parameter starting position.

Maximum likelihood using these kinds of methods can be something of a dark art. Results can be quite sensitive to the starting position and exact parameters used, so if you need precision ML then you should carefully explore the robustness of your results.

These samplers are wrapped in the current version of scipy:

• Nelder-Mead

• Powell

• CG

• BFGS

• Newton-CG

• Anneal (deprecated by scipy)

• L-BFGS-B

• TNC

• COBYLA

• SLSQP

• dogleg

• trust-ncg

Each has different (dis)advantages, and which works best will depend on your particular application. The default in CosmoSIS is Nelder-Mead. See the references on the scipy URL above for more details.

Some methods can also output an estimated covariance matrix at the likelihood peak.

##Installation

Requires SciPy 0.14 or above. This is installed by the CosmoSIS bootstrap, but if you are installing manually you can get it with the command:

pip install scipy #to install centrally, may require sudo

pip install scipy --user #to install just for you

##Parameters

These parameters can be set in the sampler's section in the ini parameter file.
If no default is specified then the parameter is required. A listing of "(empty)" means a blank string is the default.