Sampling-Free Probabilistic Deep State-Space Models
Andreas Look Melih Kandemir Barbara Rakitsch Jan Peters
Many real-world dynamical systems can be described as State-Space Models
(SSMs). In this formulation, each observation is emitted by a latent state,
which follows first-order Markovian dynamics. A Probabilistic Deep SSM
(ProDSSM) generalizes this framework to dynamical systems of unknown parametric
form, where the transition and emission models are described by neural networks
with uncertain weights. In this work, we propose the first deterministic
inference algorithm for models of this type. Our framework allows efficient
approximations for training and testing. We demonstrate in our experiments that
our new method can be employed for a variety of tasks and enjoys a superior
balance between predictive performance and computational budget.