Mcmc.MarginalLikelihood

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

Documentation

type MarginalLikelihood = Log Double

Marginal likelihood values are stored in log domain.

newtype NPoints

The number of points used to approximate the path integral.

Constructors

NPoints

Fields

• fromNPoints :: Int

Instances

Eq NPoints

Defined in Mcmc.MarginalLikelihood

Methods

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

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

Read NPoints

Defined in Mcmc.MarginalLikelihood

Methods

readsPrec :: Int -> ReadS NPoints

readList :: ReadS [NPoints]

readPrec :: ReadPrec NPoints

readListPrec :: ReadPrec [NPoints]

Show NPoints

Defined in Mcmc.MarginalLikelihood

Methods

showsPrec :: Int -> NPoints -> ShowS

show :: NPoints -> String

showList :: [NPoints] -> ShowS

data MLAlgorithm

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

Instances

Eq MLAlgorithm

Defined in Mcmc.MarginalLikelihood

Methods

(==) :: MLAlgorithm -> MLAlgorithm -> Bool

(/=) :: MLAlgorithm -> MLAlgorithm -> Bool

Read MLAlgorithm

Defined in Mcmc.MarginalLikelihood

Methods

readsPrec :: Int -> ReadS MLAlgorithm

readList :: ReadS [MLAlgorithm]

readPrec :: ReadPrec MLAlgorithm

readListPrec :: ReadPrec [MLAlgorithm]

Show MLAlgorithm

Defined in Mcmc.MarginalLikelihood

Methods

showsPrec :: Int -> MLAlgorithm -> ShowS

show :: MLAlgorithm -> String

showList :: [MLAlgorithm] -> ShowS

data MLSettings

Settings of the marginal likelihood estimation.

Constructors

MLSettings

Fields

• mlAnalysisName :: AnalysisName
• mlAlgorithm :: MLAlgorithm
• mlNPoints :: NPoints
• mlInitialBurnIn :: BurnInSettings

  Initial burn in at the starting point of the path.

• mlPointBurnIn :: BurnInSettings

  Repetitive burn in at each point on the path.

• mlIterations :: Iterations

  The number of iterations performed at each point.

• mlExecutionMode :: ExecutionMode
• mlLogMode :: LogMode
• mlVerbosity :: Verbosity

Instances

Eq MLSettings

Defined in Mcmc.MarginalLikelihood

Methods

(==) :: MLSettings -> MLSettings -> Bool

(/=) :: MLSettings -> MLSettings -> Bool

Read MLSettings

Defined in Mcmc.MarginalLikelihood

Methods

readsPrec :: Int -> ReadS MLSettings

readList :: ReadS [MLSettings]

readPrec :: ReadPrec MLSettings

readListPrec :: ReadPrec [MLSettings]

Show MLSettings

Defined in Mcmc.MarginalLikelihood

Methods

showsPrec :: Int -> MLSettings -> ShowS

show :: MLSettings -> String

showList :: [MLSettings] -> ShowS

HasVerbosity MLSettings

Defined in Mcmc.MarginalLikelihood

Methods

getVerbosity :: MLSettings -> Verbosity

HasLogMode MLSettings

Defined in Mcmc.MarginalLikelihood

Methods

getLogMode :: MLSettings -> LogMode

HasAnalysisName MLSettings

Defined in Mcmc.MarginalLikelihood

Methods

getAnalysisName :: MLSettings -> AnalysisName

HasExecutionMode MLSettings

Defined in Mcmc.MarginalLikelihood

Methods

getExecutionMode :: MLSettings -> ExecutionMode

marginalLikelihood

Arguments

:: ToJSON a

=> MLSettings

-> PriorFunction a

-> LikelihoodFunction a

-> Cycle a

-> Monitor a

-> InitialState a

-> GenIO

A source of randomness. For reproducible runs, make sure to use generators with the same seed.

-> IO MarginalLikelihood

Estimate the marginal likelihood.