In the univariate case (using pm.Normal), the switch statement works as expected:

with pm.Model() as example:
theta = pm.Normal("theta", 0,0.1)
constraint = at.lt(theta, 10) ## this should (almost) always be True
x = pm.math.switch(
constraint,
at.as_tensor_variable(np.array([-np.inf,-np.inf], "float64")),
np.array([0,1])
)
print(constraint.eval())
print(x.eval())
llh = pm.math.switch(
constraint,
at.as_tensor_variable(np.array(-np.inf, "float64")),
pm.math.sum(pm.logp(pm.Normal.dist(mu=np.zeros(2), sigma=np.ones(2)), x)) #(Univariate case)
#pm.logp(pm.MvNormal.dist(mu=np.zeros(2), cov=np.diag(np.ones(2))), x) # (*) (Multivariate case)
)
pm.Potential("llh", llh)

Then llh.eval() correctly returns -inf.

In the multivariate case (uncomment line with (*)), the model compiles fine, but llh.eval() raises an error due to the inf values in the pm.logp. But these should not be evalulated anyway due to the switch statement.

Switch always evaluates both branches, regardless of the value of constraint. See here for details (section ifelse vs switch).

You could try using ifelse in place of switch, but it might be better to just assign a large finite value like -1e6 inside the switch to avoid the inf.

Ahh yes, I was aware that switch evaluates both branches, but didn’t think it was a problem since the univariate case was returning infs with no problems. I used the switch statement because I need to apply over a long list of values and I believe switch is a lot faster since it vectorises the operation.

I realise that actually the problem arises because pm.Normal.dist accepts being evaluated at inf, whereas the multivariate case throws up errors. Do you think it is worth allowing for pm.MvNormal.dist to be evaluated at inf? It’s a sure fire way of making the sampler reject.

I will use your suggestion of a large finite value for now.
Thanks!

I looked a bit more carefully at the problem, and I think you might be right, but I’m also a bit confused.

I’m confused because you set the value of x, the observation, to be -inf, as well as the logp of the observation. It should suffice to just set the logp to -inf and leave the observation alone. For example, this code works fine, and will always reject values of theta below 10:

You might be right that perhaps the logp function for the MvNormal should map values of -inf to logp of -inf. The only reason it doesn’t now is because there is no check for infinity in this function. There is one check to ensure that the covariance matrix is valid, but none that the data (or the mean vector) are finite.

Personally I think it’s probably OK that the program fails loudly when you try to evaluate the logp of infinity, but it’s odd that the univariate distribution doesn’t.

thanks for the detailed reply!
Yes, I am doubling up on the constraint there. I wasn’t sure which would result in the most efficient inference, but since the llh will have a step gradient at the constraint then that should suffice. In my actual case I actually have two separate constraints, one which affects the data values x (and through it the llh), and one which directly affects the llh (due to parameter constraints). I suppose I could combine them all into the llh via:

I don’t know why a model constraint would ever alter the observed data (it’s already been observed after all), so I would always have x enter the likelihood as-is.

Another alternative for handing the two separate constraints is to use at.or_ and/or at.and_ to check the two constraints separately in your switch. There might be bugs that arise from adding them together if they mutually off-set in just the right way (but also maybe that’s impossible and I just don’t know it).