TransportMaps.Samplers.MarkovChainSamplers
¶
Module Contents¶
Classes¶
Metropolis-Hastings with independent proposal sampler of distribution |
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Metropolis-Hastings sampler of distribution |
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Metropolis-Hastings within Gibbs sampler of distribution |
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Hamiltonian Monte Carlo sampler of distribution |
- class TransportMaps.Samplers.MarkovChainSamplers.MetropolisHastingsIndependentProposalsSampler(d, d_prop)[source]¶
Bases:
TransportMaps.Samplers.SamplerBase.Sampler
Metropolis-Hastings with independent proposal sampler of distribution
d
, with proposal distributiond_prop
- Parameters:
d (Distributions.Distribution) – distribution to sample from
d_prop (Distributions.Distribution) – proposal distribution
- class TransportMaps.Samplers.MarkovChainSamplers.MetropolisHastingsSampler(d, d_prop)[source]¶
Bases:
TransportMaps.Samplers.SamplerBase.Sampler
Metropolis-Hastings sampler of distribution
d
, with proposald_prop
- Parameters:
d (Distributions.Distribution) – distribution \(\pi({\bf x})\) to sample from
d_prop (Distributions.ConditionalDistribution) – conditional distribution \(\pi({\bf y}\vert{\bf x})\) to use as a proposal
- class TransportMaps.Samplers.MarkovChainSamplers.MetropolisHastingsWithinGibbsSampler(d, d_prop_list, block_list=None, block_prob_list=None)[source]¶
Bases:
TransportMaps.Samplers.SamplerBase.Sampler
Metropolis-Hastings within Gibbs sampler of distribution
d
, with proposald_prop
and Gibbs block samplingblocks
- Parameters:
- class TransportMaps.Samplers.MarkovChainSamplers.HamiltonianMonteCarloSampler(d)[source]¶
Bases:
TransportMaps.Samplers.SamplerBase.Sampler
Hamiltonian Monte Carlo sampler of distribution
d
, with proposal distributiond_prop
This sampler requires the package pyhmc.
- Parameters:
d (Distributions.Distribution) – distribution to sample from