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 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) #η = 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.