Hi,

This is the first time I am trying out a Bayesian model, so please bear with me.

I am trying to perform Weibull Regression with some covariates.

I have Standardised the dataset and I am trying out the following model:

```
n_dim = training_data_norm.shape[1]
X_ = shared(training_data_norm)
with pm.Model() as model:
# Priors for unknown model parameters
k = pm.Gamma('k', alpha=10, beta=100)
beta = pm.Normal('beta', mu=0, sd=0.001, shape=n_dim)
#Î· = beta.dot(X_.T)
# Expected value of lambda parameter
lambda_obs = pm.Deterministic('beta_', \
tt.nnet.relu(beta.dot(X_.T), alpha=0.001))
# Likelihood (sampling distribution) of observations
runningtime_obs = pm.Weibull('runningtime_obs', alpha=k, \
beta=lambda_obs, observed=y_train)
```

While sampling, using the following code:

```
with model:
start = find_MAP()
trace = pm.sample(500, start=start)
```

I am getting the following error:

```
Bad initial energy, check any log probabilities that are inf or -inf, nan or very small:
runningtime_obs -inf
SamplingError: Bad initial energy
```

Could anyone help me out in figuring the source of error.