Matrix analysis
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
A model for reasoning about persistence and causation
Computational Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients
Expert Systems with Applications: An International Journal
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Sequential statistical models such as dynamic Bayesian networks and hidden Markov models more specifically, model stochastic processes over time. In this paper, we study for these models the effect of consecutive similar observations on the posterior probability distribution of the represented process. We show that, given such observations, the posterior distribution converges to a limit distribution. Building upon the rate of the convergence, we further show that, given some wished-for level of accuracy, part of the inference can be forestalled. To evaluate our theoretical results, we study their implications for a real-life model from the medical domain and for a benchmark model for agricultural purposes. Our results indicate that whenever consecutive similar observations arise, the computational requirements of inference in Markovian models can be drastically reduced.