mcmc-0.8.2.0: Sample from a posterior using Markov chain Monte Carlo
Copyright2021 Dominik Schrempf
LicenseGPL-3.0-or-later
Maintainerdominik.schrempf@gmail.com
Stabilityunstable
Portabilityportable
Safe HaskellSafe-Inferred
LanguageHaskell2010

Mcmc.MarginalLikelihood

Description

Creation date: Mon Jan 11 16:34:18 2021.

Synopsis

Documentation

type MarginalLikelihood = Log Double Source #

Marginal likelihood values are stored in log domain.

newtype NPoints Source #

The number of points used to approximate the path integral.

Constructors

NPoints 

Fields

Instances

Instances details
Read NPoints Source # 
Instance details

Defined in Mcmc.MarginalLikelihood

Show NPoints Source # 
Instance details

Defined in Mcmc.MarginalLikelihood

Eq NPoints Source # 
Instance details

Defined in Mcmc.MarginalLikelihood

Methods

(==) :: NPoints -> NPoints -> Bool #

(/=) :: NPoints -> NPoints -> Bool #

data MLAlgorithm Source #

Algorithms to calculate the marginal likelihood.

Constructors

ThermodynamicIntegration

Use a classical path integral. Also known as thermodynamic integration. In particular, Annealing-Melting Integration is used.

See Lartillot, N., & Philippe, H., Computing Bayes Factors Using Thermodynamic Integration, Systematic Biology, 55(2), 195–207 (2006). http://dx.doi.org/10.1080/10635150500433722

SteppingStoneSampling

Use stepping stone sampling.

See Xie, W., Lewis, P. O., Fan, Y., Kuo, L., & Chen, M., Improving marginal likelihood estimation for Bayesian phylogenetic model selection, Systematic Biology, 60(2), 150–160 (2010). http://dx.doi.org/10.1093/sysbio/syq085

Or Fan, Y., Wu, R., Chen, M., Kuo, L., & Lewis, P. O., Choosing among partition models in bayesian phylogenetics, Molecular Biology and Evolution, 28(1), 523–532 (2010). http://dx.doi.org/10.1093/molbev/msq224

data MLSettings Source #

Settings of the marginal likelihood estimation.

Constructors

MLSettings 

Fields

marginalLikelihood :: ToJSON a => MLSettings -> PriorFunction a -> LikelihoodFunction a -> Cycle a -> Monitor a -> InitialState a -> StdGen -> IO MarginalLikelihood Source #

Estimate the marginal likelihood.