Publications

We demonstrate a fully Bayesian pipeline to approach particle physics modelling problems with.

We demonstrate that even the simplest neural networks have truly multimodal solutions and it is only by combining these candidate models that the true probabilistic description can emerge.

We motivate a connection between Bayesian evidence and particle physics cross sections, and use this to introduce a new path for particle physics calculations