How to create a Bayesian model with Number of Markov chain iterations value, Number of Burn-in iterations and Number of thining iterations as arguments. And then how to get geweke results from the model?

Thanks for your response.

```
pymc3.sampling.sample(draws=1000, step=None, init='auto', n_init=200000, start=None, trace=None, chain_idx=0, chains=None, cores=None, tune=1000, progressbar=True, model=None, random_seed=None, discard_tuned_samples=True, compute_convergence_checks=True, callback=None, *, return_inferencedata=None, idata_kwargs: dict = None, mp_ctx=None, pickle_backend: str = 'pickle', **kwargs)
```

here i cant find brun-in iteration and number of thining itereation. Could you please help with that?

`tune`

is the burn-in, thinning you need to do that manually (i.e., slicing the array).

Thank you for your response. I still didn’t get how to find thinning. I am totally new to this. can you please help me with some more explanation.

It goes something like:

```
with pm.Model():
# you model code
trace = pm.sample(num_samples, tune=num_burn_in, cores=num_chain, return_inferencedata=True)
trace_thinned = trace.posterior.sel(draw=slice(0, num_samples, num_thinning))
```

Re thinning, this is not a recommended practice any more since it doesnt improves NUTS/HMC samples

It crashed my system. When i put tune=500 and cores=100000

`cores`

are number of cores/thread you want to run your chains in parallel, it should be equal or smaller than the number of cores you have on your CPU.

Maybe this is less confusing

```
with pm.Model():
# you model code
trace = pm.sample(num_samples, tune=num_burn_in, chains=num_chain, return_inferencedata=True)
trace_thinned = trace.posterior.sel(draw=slice(0, num_samples, num_thinning))
```

This is from @OriolAbril:

you can even do

```
trace_thinned = trace.sel(draw=slice(0, None, num_thinning))
```

to thin all groups (posterior, posterior_predictive, sample_stats , prior…) at the same time and without knowing the total number of draws