mcmc-0.8.3.0: 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.Proposal.Slide

Description

Creation date: Wed May 6 10:59:13 2020.

Synopsis

Documentation

slide :: Mean Double -> StandardDeviation Double -> PName -> PWeight -> Tune -> Proposal Double Source #

Additive proposal.

A normal distribution is used to sample the addend.

slideSymmetric :: StandardDeviation Double -> PName -> PWeight -> Tune -> Proposal Double Source #

See slide.

Use a normal distribution with mean zero. This proposal is fast, because the Metropolis-Hastings-Green ratio does not include calculation of the forwards and backwards kernels.

slideUniformSymmetric :: Size -> PName -> PWeight -> Tune -> Proposal Double Source #

See slide.

Use a uniformly distributed kernel with mean zero. This proposal is fast, because the Metropolis-Hastings-Green ratio does not include calculation of the forwards and backwards kernels.

slideContrarily :: Mean Double -> StandardDeviation Double -> PName -> PWeight -> Tune -> Proposal (Double, Double) Source #

See slide.

Use a normally distributed kernel.

The two values are slid contrarily so that their sum stays constant. Contrary proposals are useful when parameters are confounded.