Bart van Erp

## Example signal model Consider the probabilistic model: ``` X ~ GMM(z_x, mu_x1, lambda_x1, ..., mu_xN, lambda_xN) Y ~ GMM(z_y, mu_y1, lambda_y1, ..., mu_yM, lambda_yM) Z = X + Y...

Custom A*X .+ b returning wrong results

1

First of all, great package! I was trying to implement a slight alteration to the matrix multiplication example from the `Readme.md`: ```julia function custom_gemm!(C::Matrix{T}, A::Matrix{T}, B::Matrix{T}, b::Vector{T}) where { T...

Implement Gaussian Mixture Model distribution

2

For some applications, we might be interested in using mixtures of Gaussians for modeling data. Therefore this feature would be a nice addition to the toolbox.

With our recent acceptance to the SiPS conference on _Efficient Model Evidence Computation in Tree-structured Factor Graphs_, we would like to formalize the use of scale factors as prototyped in...

Merge elementary factor nodes

1

With the recent addition of the equality node, it might be interesting to explore whether elementary nodes can be merged for more efficient processing. Specifically I am referring to Table...

Introducing addons for rules (+scale factors, +switch node, +mixture model)

1

For solving #172

Speed up backward message for `Linearization`

1

The computation of the backward message for the `Linearization` approximation around the `Delta` node can be sped up significantly, without the need of computing the marginals over all inputs. This...

Add inplace `mul!` with `StandardBasisVector`

6

Currently we can only perform multiplications with the `StandardBasisVector` by creating a new vector/matrix. For performance reasons it might be beneficial to also support inplace operations here.

When using `PointMass` constraints in SP, the behaviour is incorrect as in [this issue](https://github.com/biaslab/RxInfer.jl/issues/32#issue-1464682084) on `RxInfer`. Patching this using `RequireMarginals` on both adjacent nodes also results in either not updating...

Keyword arguments not supported in non-linear node

3

The non-linear node does not support keyword arguments, e.g. ```julia function foo(x1, x2; b=0) return x1 + x2 + b end @model function model_test(b) z1 ~ NormalMeanVariance(0.0, 1.0) z2 ~...