  We reframe common tasks in jet physics in probabilistic terms, including jet
reconstruction, Monte Carlo tuning, matrix element - parton shower matching for
large jet multiplicity, and efficient event generation of jets in complex,
signal-like regions of phase space. We also introduce Ginkgo, a simplified,
generative model for jets, that facilitates research into these tasks with
techniques from statistics, machine learning, and combinatorial optimization.
We review some of the recent research in this direction that has been enabled
with Ginkgo. We show how probabilistic programming can be used to efficiently
sample the showering process, how a novel trellis algorithm can be used to
efficiently marginalize over the enormous number of clustering histories for
the same observed particles, and how dynamic programming, A* search, and
reinforcement learning can be used to find the maximum likelihood clustering in
this enormous search space. This work builds bridges with work in hierarchical
clustering, statistics, combinatorial optmization, and reinforcement learning.
