TransportMaps.Samplers¶
Classes

Class | Description |
---|---|
Sampler | Generic sampler of distribution d |
ImportanceSampler | Importance sampler of distribution d , with biasing distribution d_bias |
RejectionSampler | Rejection sampler of distribution d , with biasing distribution d_bias |
MetropolisHastingsIndependentProposalsSampler | Metropolis-Hastings with independent proposal sampler of distribution d , with proposal distribution d_prop |
MetropolisHastingsSampler | Metropolis-Hastings sampler of distribution d , with proposal d_prop |
MetropolisHastingsWithinGibbsSampler | Metropolis-Hastings within Gibbs sampler of distribution d , with proposal d_prop and Gibbs block sampling blocks |
HamiltonianMonteCarloSampler | Hamiltonian Monte Carlo sampler of distribution d , with proposal distribution d_prop |
Functions
Function | Description |
---|---|
ess | Compute the Effective Sample Size (ESS) of a sample |
Documentation
-
class
TransportMaps.Samplers.
Sampler
(d)[source]¶ Generic sampler of distribution
d
This main class just mirrors all the sampling methods provided by the distribution
d
.Parameters: d (Distributions.Distribution) – distribution to sample from.
-
class
TransportMaps.Samplers.
ImportanceSampler
(d, d_bias)[source]¶ Importance sampler of distribution
d
, with biasing distributiond_bias
Parameters: - d (Distributions.Distribution) – distribution to sample from
- d_bias (Distributions.Distribution) – biasing distribution
-
class
TransportMaps.Samplers.
RejectionSampler
(d, d_bias)[source]¶ Rejection sampler of distribution
d
, with biasing distributiond_bias
Parameters: - d (Distributions.Distribution) – distribution to sample from
- d_bias (Distributions.Distribution) – biasing distribution
-
class
TransportMaps.Samplers.
MetropolisHastingsIndependentProposalsSampler
(d, d_prop)[source]¶ 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.
MetropolisHastingsSampler
(d, d_prop)[source]¶ 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.
MetropolisHastingsWithinGibbsSampler
(d, d_prop_list, block_list=None)[source]¶ Metropolis-Hastings within Gibbs sampler of distribution
d
, with proposald_prop
and Gibbs block samplingblocks
Parameters: - d (Distributions.Distribution) – distribution \(\pi({\bf x})\) to sample from
- d_prop (
list
ofDistributions.ConditionalDistribution
) – conditional distribution \(\pi({\bf y}\vert{\bf x})\) to use as a proposal - block_list (
list
oflist
) – list of blocks of variables
-
class
TransportMaps.Samplers.
HamiltonianMonteCarloSampler
(d)[source]¶ 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
-
TransportMaps.Samplers.
ess
(samps, quantile=0.99, do_xcorr=False, plotting=False, plot_lag=50, fig=None)[source]¶ Compute the Effective Sample Size (ESS) of a sample
The minimum ESS over all the dimension is returned. Cross-correlation can be optionally used as well in the determination of the ESS. Plotting of the correlation decay can be shown.
The ESS is computed as \(\lfloor m/\kappa \rfloor\), where
\[\kappa = 1 + \sum_{c_i>b_i} c_i \;,\]\(c_i\) is the auto-correlation at lag \(i\) and \(b_i\) is the
quantile
-confidence interval for the \(i\)-th value of auto-correlation (i.e. only significant auto-correlation values are summed up).Parameters: - samps (
ndarray
[\(m,d\)]) – \(d\)-dimensional sample on which to compute the ESS - quantile (float) – condifence interval quantile
- do_xcorr (bool) – whether to compute and use the auto-correlation function
- plotting (bool) – whether to plot auto/cross-correlation decays
- plot_lag (int) – how many lags to plot
- fig (figure) – handle to a figure
Returns: (
int
) – minimum ESS across the \(d\) dimensions- samps (