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A Dynamic Bayesian Network (DBN) is a Bayesian network which relates variables to each other over adjacent time steps. This is often called a Two-Timeslice BN (2TBN) because it says that at any point in time T, the value of a variable can be calculated from the internal regressors and the immediate prior value (time T-1). DBNs were developed by Paul Dagum in the early 1990s when he led research funded by two National Science Foundation grants at Stanford University's Section on Medical Informatics. Dagum developed DBNs to unify and extend traditional linear state-space models such as Kalman filters, linear and normal forecasting models such as ARMA and simple dependency models such as hidden Markov models into a general probabilistic representation and inference mechanism for arbitrary non

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  • Dynamic Bayesian network
  • Réseau bayésien dynamique
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  • Un réseau bayésien dynamique ou temporel (souvent noté RBD, ou DBN pour Dynamic Bayesian Network) est un modèle statistique et stochastique qui étend la notion de réseau bayésien. À la différence de ces derniers, un réseau bayésien dynamique permet de représenter l'évolution des variables aléatoires en fonction d'une séquence discrète, par exemple des pas temporels. Le terme dynamique caractérise le système modélisé, et non le réseau qui lui ne change pas.
  • A Dynamic Bayesian Network (DBN) is a Bayesian network which relates variables to each other over adjacent time steps. This is often called a Two-Timeslice BN (2TBN) because it says that at any point in time T, the value of a variable can be calculated from the internal regressors and the immediate prior value (time T-1). DBNs were developed by Paul Dagum in the early 1990s when he led research funded by two National Science Foundation grants at Stanford University's Section on Medical Informatics. Dagum developed DBNs to unify and extend traditional linear state-space models such as Kalman filters, linear and normal forecasting models such as ARMA and simple dependency models such as hidden Markov models into a general probabilistic representation and inference mechanism for arbitrary non
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  • A Dynamic Bayesian Network (DBN) is a Bayesian network which relates variables to each other over adjacent time steps. This is often called a Two-Timeslice BN (2TBN) because it says that at any point in time T, the value of a variable can be calculated from the internal regressors and the immediate prior value (time T-1). DBNs were developed by Paul Dagum in the early 1990s when he led research funded by two National Science Foundation grants at Stanford University's Section on Medical Informatics. Dagum developed DBNs to unify and extend traditional linear state-space models such as Kalman filters, linear and normal forecasting models such as ARMA and simple dependency models such as hidden Markov models into a general probabilistic representation and inference mechanism for arbitrary nonlinear and non-normal time-dependent domains. Today, DBNs are common in robotics, and have shown potential for a wide range of data mining applications. For example, they have been used in speech recognition, digital forensics, protein sequencing, and bioinformatics. DBN is a generalization of hidden Markov models and Kalman filters.
  • Un réseau bayésien dynamique ou temporel (souvent noté RBD, ou DBN pour Dynamic Bayesian Network) est un modèle statistique et stochastique qui étend la notion de réseau bayésien. À la différence de ces derniers, un réseau bayésien dynamique permet de représenter l'évolution des variables aléatoires en fonction d'une séquence discrète, par exemple des pas temporels. Le terme dynamique caractérise le système modélisé, et non le réseau qui lui ne change pas.
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