Comparison to other packages

Nowadays there's plenty of probabilistic programming languages and packages available. Although all are based on Bayesian inference, their methodologies vary. This section compares RxInfer.jl against other renowned probabilistic programming languages and packages. The goal is to enlighten potential users about the nuances and guide them in choosing the package that best suits their requirements.

(Date of creation: 20/10/2023)

Legend

• ✓ : Full capability or feature is present.
• ~ : Partial capability or feature is present.
• ✗ : No capability or feature.

Notes:

• Universality: Denotes the capability to depict a vast array of probabilistic models.
• Efficiency: Highlights computational competence. A "~" in this context suggests perceived slowness.
• Expressiveness: Assesses the ability to concisely formulate intricate probabilistic models.
• Debugging & Visualization: Evaluates the suite of tools for model debugging and visualization.
• Modularity: Reflects the potential to create models by integrating smaller models.
• Inference Engines: Pinpoints the primary inference strategy employed by the toolbox.
• Language: Identifies the programming language integral to the toolbox.
• Community & Ecosystem: Signifies the vibrancy of the ecosystem, inclusive of tools, libraries, and community backing.

RxInfer.jl breakdown

• Universality: RxInfer.jl shines in formulating models derived from the exponential family distributions. The package encompasses not only commonly used distributions such as Gaussian or Bernoulli, but also specialized stochastic nodes that represents prevalent probabilistic models like Autoregressive models, Gamma Mixture models, among others. Furthermore, RxInfer.jl proficiently manages deterministic transformations of variables from the exponential family, see Delta node. Nevertheless, for models outside the exponential family, RxInfer.jl might not be the good choice. Such models would require the creation of novel nodes and corresponding rules, as illustrated in this section.

• Efficiency: RxInfer.jl distinguishes itself with its inference engine rooted in reactive message passing. This approach is supremely efficient, facilitating real-time propagation of updates across the system, supporting parallelization, interruptibility, and more.

• Modularity: Broadly, the toolboxes in the table aren't modular in the truest sense. They don't offer the fusion of models by integrating smaller models. RxInfer.jl on the other hand provides a way to compose different models:

@model function inner_inner(τ, y, x)
    y ~ Normal(mean = τ[1], var = τ[2] + x)
end

@model function inner(θ, α)
    β ~ Normal(mean = 0.0, var = 1.0)
    α ~ Gamma(shape = β, rate = 1.0)
    α ~ inner_inner(τ = θ, x = 3)
end

@model function outer()
    local w
    for i = 1:5
        w[i] ~ inner(θ = Gamma(shape = 1.0, rate = 1.0))
    end
    y ~ inner(θ = w[2:3])
end

• Expressiveness: RxInfer.jl empowers users to elegantly and concisely craft models, closely mirroring probabilistic notation, thanks to Julia's macro capabilities. To illustrate this, let's consider the following model:

\[\begin{aligned} x & \sim \mathrm{Normal}(0.0, 1.0)\\ w & \sim \mathrm{InverseGamma}(1.0, 1.0)\\ y & \sim \mathrm{Normal}(x, w) \end{aligned}\]

The model then is expressed in RxInfer.jl as follows:

@model function example_model()
    x ~ Normal(mean = 0.0, var = 1.0)
    w ~ InverseGamma(α = 1, θ = 1)
    y ~ Normal(mean = x, var = w)
end

• Debugging & Visualization: RxInfer.jl does provide a mechanism to debug the inference procedure and visualise the graph structure, even though not as seamlessly as some other packages.