arviz.mcse#

arviz.mcse(data, *, var_names=None, method='mean', prob=None, dask_kwargs=None)[source]#

Calculate Markov Chain Standard Error statistic.

Parameters:

dataobj: Any object that can be converted to an arviz.InferenceData object Refer to documentation of arviz.convert_to_dataset() for details For ndarray: shape = (chain, draw). For n-dimensional ndarray transform first to dataset with az.convert_to_dataset.
var_nameslist: Names of variables to include in the rhat report
methodstr: Select mcse method. Valid methods are: - “mean” - “sd” - “median” - “quantile”
probfloat: Quantile information.
dask_kwargsdict, optional: Dask related kwargs passed to wrap_xarray_ufunc().

Returns:

xarray.Dataset: Return the msce dataset

See also

ess: Compute autocovariance estimates for every lag for the input array.
summary: Create a data frame with summary statistics.
plot_mcse: Plot quantile or local Monte Carlo Standard Error.

Examples

Calculate the Markov Chain Standard Error using the default arguments:

In [1]: import arviz as az
   ...: data = az.load_arviz_data("non_centered_eight")
   ...: az.mcse(data)
   ...: 
Out[1]: 
<xarray.Dataset> Size: 656B
Dimensions:  (school: 8)
Coordinates:
  * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
Data variables:
    mu       float64 8B 0.08102
    theta_t  (school) float64 64B 0.02339 0.01925 0.02092 ... 0.01931 0.01906
    tau      float64 8B 0.0791
    theta    (school) float64 64B 0.1285 0.103 0.1306 ... 0.1158 0.1193 0.1218
Attributes:
    created_at:                 2022-10-13T14:37:26.351883
    arviz_version:              0.13.0.dev0
    inference_library:          pymc
    inference_library_version:  4.2.2
    sampling_time:              4.738754749298096
    tuning_steps:               1000

Calculate the Markov Chain Standard Error using the quantile method:

In [2]: az.mcse(data, method="quantile", prob=0.7)
Out[2]: 
<xarray.Dataset> Size: 656B
Dimensions:  (school: 8)
Coordinates:
  * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
Data variables:
    mu       float64 8B 0.1305
    theta_t  (school) float64 64B 0.034 0.02491 0.0319 ... 0.02363 0.03383
    tau      float64 8B 0.1145
    theta    (school) float64 64B 0.1776 0.1047 0.1426 ... 0.156 0.1508 0.1209
Attributes:
    created_at:                 2022-10-13T14:37:26.351883
    arviz_version:              0.13.0.dev0
    inference_library:          pymc
    inference_library_version:  4.2.2
    sampling_time:              4.738754749298096
    tuning_steps:               1000