Mapping disease risk across a region, modelling how insurance claims accumulate, tracking a fish stock as it rises and falls: these come from different fields, but the underlying models are alike.
Each describes a smooth, structured process that we never see directly, only through noisy data.
The standard approach is to hand these models to a general-purpose sampler and wait.
The trouble is that the sampler sees a black box, and brute-forces structure that the model states plainly.
By contrast, a family of specialized methods uses this structure, and they turn out to form one spectrum, from fast (orders of magnitude faster than MCMC) to faithful.
This talk introduces Latte.jl, a probabilistic programming language for latent Gaussian models that unifies this spectrum of structure-exploiting inference methods.