Samples

class cuqi.samples.Samples(samples, geometry=None, is_par=True, is_vec=True)

An object used to store samples from distributions.

Parameters:

• samples (ndarray) – Contains the raw samples as a numpy array indexed by the last axis of the array.

• geometry (cuqi.geometry.Geometry, default None) – Contains the geometry related of the samples

__init__(samples, geometry=None, is_par=True, is_vec=True)

Methods

__init__(samples[, geometry, is_par, is_vec])
burnthin(Nb[, Nt])	Remove burn-in and thin samples.
ci_width([percent])	Compute width of the pointwise credibility intervals of the samples
compute_ci([percent])	Compute pointwise credibility intervals of the samples.
compute_ess(**kwargs)	Compute effective sample size (ESS) of samples.
compute_rhat(chains, **kwargs)	Compute rhat value of samples given list of cuqi.samples.Samples objects (chains) to compare with.
diagnostics()	Conducts diagnostics on the chain (Geweke test).
hist_chain(variable_indices, *args, **kwargs)	Plots samples histogram of variables with indices specified in variable_indices.
mean()	Compute mean of the samples.
median()	Compute pointwise median of the samples
plot([sample_indices])	Plots one or more samples.
plot_autocorrelation([variable_indices, ...])	Plot the autocorrelation function of one or more variables in a single chain.
plot_chain([variable_indices])
plot_ci([percent, exact, plot_envelope_kwargs])	Plots the credibility interval for the samples according to the geometry.
plot_ci_width([percent])	Plot width of the pointwise credibility intervals of the samples
plot_mean(*args, **kwargs)	Plot pointwise mean of the samples
plot_median(*args, **kwargs)	Plot pointwise median of the samples
plot_pair([variable_indices, kind, marginals])	Plot marginals using a scatter, kde and/or hexbin matrix.
plot_std(*args, **kwargs)	Plot pointwise standard deviation of the samples
plot_trace([variable_indices, exact, ...])	Creates a traceplot of the samples consisting of 1) a histogram/density plot of the samples and 2) an MCMC chain plot.
plot_variance(*args, **kwargs)	Plot pointwise variance of the samples
plot_violin([variable_indices])	Create a violin plot of the samples.
std()	Compute pointwise standard deviation of the samples
to_arviz_inferencedata([variable_indices])	Return arviz InferenceData object of samples for the given variable indices
variance()	Compute pointwise variance of the samples

Attributes

Ns	Return number of samples
funvals	Returns a new Samples object of sample function values.
geometry
is_vec
parameters	If self.is_par is False, returns a new Samples object of sample parameters by converting the function values to parameters.
shape	Returns the shape of samples.
vector	Returns a new Samples object of samples in vector form.