Analyzers

SimpleMC contains the following analyzers:

Analyzers

Analyzer key

Type

Description

Tasks

mcmc

Bayesian inference

Metropolis-Hastings algorithm

Parameter estimation

nested

Bayesian inference

Nested sampling algorithms from Dynesty library

[arXiv:1904.02180]

Parameter estimation and model comparison

emcee

Bayesian inference

EMCEE algorithm

[arXiv:1202.3665]

Parameter estimation

MaxLikeAnalyzer

Optimization

L-BFGS-B algorithm

https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html

Likelihood maximization

ga_deap

Optimization

Collection of genetic algorithms from DEAP library

Fortin, F. A., et al (2012).

DEAP: Evolutionary algorithms made easy.

The Journal of Machine Learning Research, 13(1), 2171-2175.

Likelihood maximization

We recommend for a previous quickly test, to use an optimizer before an Bayesian inference algorithm.

Sampler comparison

Note

To verify the consistency of the parameter estimation among the different samplers available, we have made the following graph.

_images/samplersTriangle.png

We estimates the posteriors of the parameters of the owaCDM model (dark energy with timedependent equation-of-state in a model of unknown curvature) using Supernovae type Ia, Cosmic Chronometers (Hubble Distance) and BAO .