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.

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 .