Diagnostics¶
This provides a small set of utilities in NumPyro that are used to diagnose posterior samples.
Autocorrelation¶

autocorrelation
(x, axis=0)[source]¶ Computes the autocorrelation of samples at dimension
axis
.Parameters:  x (numpy.ndarray) – the input array.
 axis (int) – the dimension to calculate autocorrelation.
Returns: autocorrelation of
x
.Return type:
Autocovariance¶

autocovariance
(x, axis=0)[source]¶ Computes the autocovariance of samples at dimension
axis
.Parameters:  x (numpy.ndarray) – the input array.
 axis (int) – the dimension to calculate autocovariance.
Returns: autocovariance of
x
.Return type:
Effective Sample Size¶

effective_sample_size
(x)[source]¶ Computes effective sample size of input
x
, where the first dimension ofx
is chain dimension and the second dimension ofx
is draw dimension.References:
 Introduction to Markov Chain Monte Carlo, Charles J. Geyer
 Stan Reference Manual version 2.18, Stan Development Team
Parameters: x (numpy.ndarray) – the input array. Returns: effective sample size of x
.Return type: numpy.ndarray
Gelman Rubin¶

gelman_rubin
(x)[source]¶ Computes Rhat over chains of samples
x
, where the first dimension ofx
is chain dimension and the second dimension ofx
is draw dimension. It is required thatx.shape[0] >= 2
andx.shape[1] >= 2
.Parameters: x (numpy.ndarray) – the input array. Returns: Rhat of x
.Return type: numpy.ndarray
Split Gelman Rubin¶

split_gelman_rubin
(x)[source]¶ Computes split Rhat over chains of samples
x
, where the first dimension ofx
is chain dimension and the second dimension ofx
is draw dimension. It is required thatx.shape[1] >= 4
.Parameters: x (numpy.ndarray) – the input array. Returns: split Rhat of x
.Return type: numpy.ndarray
HPDI¶

hpdi
(x, prob=0.9, axis=0)[source]¶ Computes “highest posterior density interval” (HPDI) which is the narrowest interval with probability mass
prob
.Parameters:  x (numpy.ndarray) – the input array.
 prob (float) – the probability mass of samples within the interval.
 axis (int) – the dimension to calculate hpdi.
Returns: quantiles of
x
at(1  prob) / 2
and(1 + prob) / 2
.Return type:
Summary¶

summary
(samples, prob=0.9, group_by_chain=True)[source]¶ Returns a summary table displaying diagnostics of
samples
from the posterior. The diagnostics displayed are mean, standard deviation, median, the 90% Credibility Intervalhpdi()
,effective_sample_size()
, andsplit_gelman_rubin()
.Parameters:  samples (dict or numpy.ndarray) – a collection of input samples with left most dimension is chain dimension and second to left most dimension is draw dimension.
 prob (float) – the probability mass of samples within the HPDI interval.
 group_by_chain (bool) – If True, each variable in samples will be treated as having shape num_chains x num_samples x sample_shape. Otherwise, the corresponding shape will be num_samples x sample_shape (i.e. without chain dimension).

print_summary
(samples, prob=0.9, group_by_chain=True)[source]¶ Prints a summary table displaying diagnostics of
samples
from the posterior. The diagnostics displayed are mean, standard deviation, median, the 90% Credibility Intervalhpdi()
,effective_sample_size()
, andsplit_gelman_rubin()
.Parameters:  samples (dict or numpy.ndarray) – a collection of input samples with left most dimension is chain dimension and second to left most dimension is draw dimension.
 prob (float) – the probability mass of samples within the HPDI interval.
 group_by_chain (bool) – If True, each variable in samples will be treated as having shape num_chains x num_samples x sample_shape. Otherwise, the corresponding shape will be num_samples x sample_shape (i.e. without chain dimension).