Markov jump processes are continuous-time stochastic processes which describe
dynamical systems evolving in discrete state spaces. These processes find wide
application in the natural sciences and machine learning, but their inference
is known to be far from trivial. In this work we introduce a methodology for
zero-shot inference of Markov jump processes (MJPs), on bounded state spaces,
from noisy and sparse observations, which consists of two components. First, a
broad probability distribution over families of MJPs, as well as over possible
observation times and noise mechanisms, with which we simulate a synthetic
dataset of hidden MJPs and their noisy observation process. Second, a neural
network model that processes subsets of the simulated observations, and that is
trained to output the initial condition and rate matrix of the target MJP in a
supervised way. We empirically demonstrate that one and the same (pretrained)
model can infer, in a zero-shot fashion, hidden MJPs evolving in state spaces
of different dimensionalities. Specifically, we infer MJPs which describe (i)
discrete flashing ratchet systems, which are a type of Brownian motors, and the
conformational dynamics in (ii) molecular simulations, (iii) experimental ion
channel data and (iv) simple protein folding models. What is more, we show that
our model performs on par with state-of-the-art models which are finetuned to
the target datasets.

使用零样本推理的神经网络模型，能够从嘈杂且稀疏的观察中，推断出具有不同维度的马尔可夫跳跃过程，并且达到与针对目标数据集微调的最先进模型相媲美的性能。