## models.mcmc_diagnostics()


MCMC diagnostics of chains of draws (Vehtari et al. 2021, as R's


Usage


``` python
models.mcmc_diagnostics(chains)
```


`posterior`): rank-normalized split R-hat, bulk and tail effective sample sizes, the effective sample size of the mean and its Monte Carlo standard error.


## Parameters


`chains: list of list of float`  
Equal-length chains, at least 4 draws each.


## Returns


`dict`  
`rhat`, `ess_bulk`, `ess_tail`, `ess_mean`, `mcse_mean`.


## Examples

``` python
>>> from prospicio.models import mcmc_diagnostics
>>> a = [float((i * 37) % 101) for i in range(400)]
>>> b = [float((i * 53 + 7) % 101) for i in range(400)]
>>> mcmc_diagnostics([a, b])["rhat"] < 1.01
```

True