Reactive Programming Model

ReactiveMP.jl is built on a reactive programming paradigm. Unlike traditional inference engines that follow a pre-defined, static computation schedule (e.g., performing a forward and backward pass), ReactiveMP.jl operates by reacting to changes in the underlying data.

The Mental Model

To use this package effectively, it helps to shift your thinking from "static values" to "dynamic streams."

1. Observables as Streams

In many algorithms, a message is just a piece of data at a specific point in time. In ReactiveMP.jl, messages and marginals are treated as Observables.

Think of an Observable not as a single number, but as a stream of values. Whenever a node performs a computation and produces a new result, it "emits" this value into the stream. Any downstream node listening to this stream will automatically receive the update.

2. Automatic Propagation

Because nodes are connected via these streams, the graph handles its own execution. You do not need to manually trigger a "message passing step." Instead:

• An external event occurs (e.g., a new observation is added via new_observation!).
• This change triggers an update in a specific node.
• That node's output changes, which automatically notifies all connected neighbors.
• The change propagates through the graph structure, only visiting nodes that are actually affected by the update.

This "dependency-driven" execution ensures that we only perform the minimum amount of computation necessary to keep the beliefs up to date.

For Deep Dives

The underlying machinery for this reactive behavior is provided by Rocket.jl. If you want to understand the low-level mechanics of how Observables, triggers, and reactive streams are implemented in Julia, we highly recommend exploring the Rocket.jl documentation.