Signal automata and hidden Markov models
arXiv:2105.01341v1
Originality Synthesis-oriented
AI Analysis
This addresses the challenge of efficient real-time inference for hidden Markov models, which is incremental as it builds on existing methods.
The authors tackled the problem of inferring dynamical hidden Markov models from time series data, achieving a model that updates in constant time with each new measurement under reasonable assumptions.
A generic method for inferring a dynamical hidden Markov model from a time series is proposed. Under reasonable hypothesis, the model is updated in constant time whenever a new measurement arrives.