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 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 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, protein sequencing, and bioinformatics. DBN have shown to produce equivalent solutions to Hidden Markov Models and Kalman Filters.
Famous quotes containing the words dynamic and/or network:
“The nearer a conception comes towards finality, the nearer does the dynamic relation, out of which this concept has arisen, draw to a close. To know is to lose.”
—D.H. (David Herbert)
“Of what use, however, is a general certainty that an insect will not walk with his head hindmost, when what you need to know is the play of inward stimulus that sends him hither and thither in a network of possible paths?”
—George Eliot [Mary Ann (or Marian)