  Continuous latent variables (LVs) are a key ingredient of many generative
models, as they allow modelling expressive mixtures with an uncountable number
of components. In contrast, probabilistic circuits (PCs) are hierarchical
discrete mixtures represented as computational graphs composed of input, sum
and product units. Unlike continuous LV models, PCs provide tractable inference
but are limited to discrete LVs with categorical (i.e. unordered) states. We
bridge these model classes by introducing probabilistic integral circuits
(PICs), a new language of computational graphs that extends PCs with integral
units representing continuous LVs. In the first place, PICs are symbolic
computational graphs and are fully tractable in simple cases where analytical
integration is possible. In practice, we parameterise PICs with light-weight
neural nets delivering an intractable hierarchical continuous mixture that can
be approximated arbitrarily well with large PCs using numerical quadrature. On
several distribution estimation benchmarks, we show that such PIC-approximating
PCs systematically outperform PCs commonly learned via expectation-maximization
or SGD.
