Tensor-train methods for sequential state and parameter learning in state-space models

Understand

We consider sequential state and parameter learning in state-space models with intractable state transition and observation processes.

• By exploiting low-rank tensor train (TT) decompositions, we propose new sequential learning methods for joint parameter and state estimation under the Bayesian framework.
• Our key innovation is the introduction of scalable function approximation tools such as TT for recursively learning the sequentially updated posterior distributions.
• The function approximation perspective of our methods offers tractable error analysis and potentially alleviates the particle degeneracy faced by many particle-based methods.