# 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. autocorrelation of x. numpy.ndarray

## 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. autocovariance of x. numpy.ndarray

## Effective Sample Size¶

effective_sample_size(x)[source]

Computes effective sample size of input x, where the first dimension of x is chain dimension and the second dimension of x is draw dimension.

References:

1. Introduction to Markov Chain Monte Carlo, Charles J. Geyer
2. Stan Reference Manual version 2.18, Stan Development Team
Parameters: x (numpy.ndarray) – the input array. effective sample size of x. numpy.ndarray

## Gelman Rubin¶

gelman_rubin(x)[source]

Computes R-hat over chains of samples x, where the first dimension of x is chain dimension and the second dimension of x is draw dimension. It is required that x.shape >= 2 and x.shape >= 2.

Parameters: x (numpy.ndarray) – the input array. R-hat of x. numpy.ndarray

## Split Gelman Rubin¶

split_gelman_rubin(x)[source]

Computes split R-hat over chains of samples x, where the first dimension of x is chain dimension and the second dimension of x is draw dimension. It is required that x.shape >= 4.

Parameters: x (numpy.ndarray) – the input array. split R-hat of x. 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. quantiles of x at (1 - prob) / 2 and (1 + prob) / 2. numpy.ndarray

## 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 Interval hpdi(), effective_sample_size(), and split_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 Interval hpdi(), effective_sample_size(), and split_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).