Hello,

have a variable phi = pm.Uniform(“phi”, 0.1,1). However, when I sample from the model, the initial value for phi is negative which leads to undefined likelihood and the failure. The full code/error is below. Not sure why I get a negative number, when the support for my distribution is strictly positive.

```
with pm.Model(coords={"predictors": X.columns.values}) as beta_reg:
beta = pm.Normal("beta", 0,1, dims="predictors")
beta0 = pm.Normal("beta0", 1, 1)
phi = pm.Uniform("phi", 0.1,1)
mu = pm.Deterministic("mu",pm.math.invlogit(beta0 + at.dot(X.values, beta)))
a = phi*mu
b = (1.0 - mu)*phi
ratio = pm.Beta("ratio", alpha = a,beta = b, observed=y.values)
pm.sample()
---------------------------------------------------------------------------
SamplingError Traceback (most recent call last)
Input In [196], in <cell line: 1>()
7 b = (1.0 - mu)*phi
8 ratio = pm.Beta("ratio", alpha = a,beta = b, observed=y.values)
----> 9 pm.sample()
File ~/miniconda3/lib/python3.9/site-packages/pymc/sampling.py:558, in sample(draws, step, init, n_init, initvals, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, **kwargs)
556 # One final check that shapes and logps at the starting points are okay.
557 for ip in initial_points:
--> 558 model.check_start_vals(ip)
559 _check_start_shape(model, ip)
561 sample_args = {
562 "draws": draws,
563 "step": step,
(...)
573 "discard_tuned_samples": discard_tuned_samples,
574 }
File ~/miniconda3/lib/python3.9/site-packages/pymc/model.py:1794, in Model.check_start_vals(self, start)
1791 initial_eval = self.point_logps(point=elem)
1793 if not all(np.isfinite(v) for v in initial_eval.values()):
-> 1794 raise SamplingError(
1795 "Initial evaluation of model at starting point failed!\n"
1796 f"Starting values:\n{elem}\n\n"
1797 f"Initial evaluation results:\n{initial_eval}"
1798 )
SamplingError: Initial evaluation of model at starting point failed!
Starting values:
{'beta': array([ 0.16049949, -0.22746862, -0.69406021, -0.89434199, -0.96045462,
0.81442739, 0.65041207]), 'beta0': array(0.10526258), 'phi_interval__': array(-0.30358915)}
Initial evaluation results:
{'beta': -8.12, 'beta0': -1.32, 'phi': -1.41, 'ratio': inf}
```