# Comparison to other packages

Nowadays there's plenty of probabilistic programming languages and packages available. While all of them 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.

**DISCLAIMER**:

- This comparison isn't exhaustive and mirrors the author's hands-on experience with the packages. Some might have undergone more rigorous testing than others. If you're an author of one of these packages and believe the comparison doesn't do justice, please reach out, and we'll be more than willing to rectify.
- The comparison is more qualitative than quantitative, considering the intricacies of upkeeping benchmarking code for perpetually evolving packages.

Toolbox | Universality | Efficiency | Expressiveness | Debugging & Visualization | Modularity | Inference Engine | Language | Community & Ecosystem |
---|---|---|---|---|---|---|---|---|

RxInfer.jl | ~ | ✓ | ✓ | ~ | ✗ | Message-passing | Julia | ✗ |

ForneyLab.jl | ✗ | ~ | ✗ | ~ | ✗ | Message-passing | Julia | ✗ |

Infer.net | ~ | ✓ | ✗ | ✓ | ✗ | Message-passing | C# | ✗ |

PGMax | ✗ | ✓ | ✗ | ✓ | ✗ | Message-passing | Python | ✗ |

Turing.jl | ✓ | ✗ | ✓ | ~ | ✗ | Sampling | Julia | ✓ |

PyMC | ✓ | ✗ | ✓ | ✓ | ✗ | Sampling | Python | ✓ |

NumPyro | ✓ | ✓ | ~ | ✓ | ✗ | Sampling | Python | ✓ |

TensorFlow Probability | ✓ | ✗ | ~ | ✓ | ✗ | Sampling | Python | ✓ |

Stan | ✓ | ✗ | ✓ | ✓ | ✗ | Sampling | Stan | ✓ |

(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 intricate 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. However, the current version of`RxInfer.jl`

hasn't harnessed all these potentials.**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. While`RxInfer.jl`

currently doesn't support this, a solution is on the horizon:

```
@model function inner_inner(τ, y)
y ~ Normal(τ[1], τ[2])
end
@model function inner(θ, α)
β ~ Normal(0, 1)
α ~ Gamma(β, 1)
α ~ inner_inner(τ = θ)
end
@model function outer()
local w
for i = 1:5
w[i] ~ inner(θ = Gamma(1, 1))
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:

```
using RxInfer
@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**: While`RxInfer.jl`

struggles with Julia's early-stage debugging system, it does provide a mechanism to debug the inference procedure, even though not as seamlessly as some other packages.