I just made public my Master Thesis project that I completed at the University of Oxford.
It is called CPProb and it is a C++ general purpose probabilistic programming library that uses a version of Variational Inference to learn proposals for Importance Sampling.
It aims to be usable directly in preexisting C++ codebases.
For the fulfillment of the Master Thesis, I also wrote a tutorial on Particle filters via SMC-like methods, and I described the design choices that one finds when implementing one of these systems.
The C++ library with the corresponding Pytorch-based neural network and the tutorial can be found in
It is called CPProb and it is a C++ general purpose probabilistic programming library that uses a version of Variational Inference to learn proposals for Importance Sampling.
It aims to be usable directly in preexisting C++ codebases. For the fulfillment of the Master Thesis, I also wrote a tutorial on Particle filters via SMC-like methods, and I described the design choices that one finds when implementing one of these systems.
The C++ library with the corresponding Pytorch-based neural network and the tutorial can be found in
https://github.com/Lezcano/CPProb
and are available under a MIT license.