## kernels.harness.SamplerSettings


One escalation stage's MCMC budget.


Usage

``` python
kernels.harness.SamplerSettings(
    chains=4,
    iter_warmup=1000,
    iter_sampling=2500,
    parallel_chains=1,
    target_accept=None,
    max_treedepth=None
)
```


`None` means "the entry's own default" and is not passed at all, so a single default-constructed stage reproduces a plain [gallery.fit()](gallery.fit.md#ibnr.gallery.fit). [fit_kwargs](kernels.harness.SamplerSettings.md#ibnr.kernels.harness.SamplerSettings.fit_kwargs) filters against the entry's signature, so the same stage list can drive a study that mixes MCMC and likelihood-based entries (the latter simply ignore all of this).


## Parameter Attributes


`chains: int = ``4`  

`iter_warmup: int = ``1000`  

`iter_sampling: int = ``2500`  

`parallel_chains: int = ``1`  

`target_accept: float | None = None`  

`max_treedepth: int | None = None`  


## Methods

| Name | Description |
|----|----|
| [fit_kwargs()](#fit_kwargs) | The subset of these settings that `fit_fn` actually accepts. |

------------------------------------------------------------------------


#### fit_kwargs()


The subset of these settings that `fit_fn` actually accepts.


Usage

``` python
fit_kwargs(fit_fn)
```