# "ValueError: Bad initial energy: inf. The model might be misspecified"

**URL:** <https://discourse.pymc.io/t/valueerror-bad-initial-energy-inf-the-model-might-be-misspecified/1623>\
**Category:** Questions\
**Created:** [July 31, 2018, 6:10pm UTC](https://discourse.pymc.io/t/valueerror-bad-initial-energy-inf-the-model-might-be-misspecified/1623 "2018-07-31T18:10:30Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![ru111](https://avatars.discourse-cdn.com/v4/letter/r/fbc32d/32.png) [@ru111](https://discourse.pymc.io/u/ru111)\
**Post date:** [July 31, 2018, 6:10pm UTC](https://discourse.pymc.io/t/valueerror-bad-initial-energy-inf-the-model-might-be-misspecified/1623/1 "2018-07-31T18:10:30Z")

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I am modelling 2 sets of parameters, c (4 x 4) and theta (1 x 4), each having a related likelihood function like the following. However when I sample

```
model = pm.Model()
with model:

    # Priors for unknown model parameters
    hyper_mu = pm.Normal('hyper_mu', mu=0, sd=10)
    hyper_sd = pm.Gamma('hyper_sd', alpha=0.01, beta=0.001)
    c = pm.Normal('c', mu=hyper_mu, sd=hyper_sd, shape=(4, 4)) # We want this

    # Prior for nuisance parameter
    theta0 = np.ones(4)
    theta = pm.Dirichlet('theta', theta0, shape=(1, 4))

   
    # Likelihoods (sampling distribution) of observations
    p = sigmoid(c)
    L1 = pm.Binomial('L1', n=N_data, p=p, observed=observed_successes)
    
    p2 = pm.math.sum(L1 * theta, axis=1) # SHAPE: (4,)
    L2 = pm.Binomial('L2', n=N_data2, p=p2, observed=successes2)

```

When I do `model.check_test_point()`, I get:

> ```
> > L2 -inf
> > L1 -7855.870000
> > c -51.540000
> > hyper_mu -3.220000
> > hyper_sd_log__ -4.660000
> > theta_stickbreaking__ -3.750000
> > Name: Log-probability of test_point, dtype: float64
> 
> ```

Which seems to suggest my `L2` initialisation is bad, but I’m not sure how to correct this… I am certain that the likelihood is given by `sum(L1 * theta, axis=1)` and I am giving the correct data (N\_data2, successes2) in the right shape (4,).

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**Author:** ![junpenglao](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/junpenglao/32/8_2.png) [@junpenglao](https://discourse.pymc.io/u/junpenglao)\
**Post date:** [July 31, 2018, 9:16pm UTC](https://discourse.pymc.io/t/valueerror-bad-initial-energy-inf-the-model-might-be-misspecified/1623/2 "2018-07-31T21:16:34Z")

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Usually this is your parameter goes out of the support, you can check whether `p2.tag.test_value` are all within 0 and 1.

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<div class="post-metadata">

**Author:** ![ru111](https://avatars.discourse-cdn.com/v4/letter/r/fbc32d/32.png) [@ru111](https://discourse.pymc.io/u/ru111)\
**Post date:** [August 1, 2018, 9:52am UTC](https://discourse.pymc.io/t/valueerror-bad-initial-energy-inf-the-model-might-be-misspecified/1623/3 "2018-08-01T09:52:20Z")

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Many thanks - this helped me understand that I was being extremely silly, I had to use `p2 = pm.math.sum(p * theta, axis=1)` not `p2 = pm.math.sum(L1 * theta, axis=1)`, I thought L1 was like normalised probability similar to p. I will go back to basic documentation! Sorry for the hassle.