Bayes Theorem and Stochastic Process Prediction
- Day - Time: 11 June 2013, h.14:30
- Place: Area della Ricerca CNR di Pisa - Room: C-29
Bayesian inference is recognized as a general framework for performing optimal stochastic process prediction. Fundamentally, it assumes that the uncertainty in our knowledge of the state of the observed system may be well represented by probabilities. Bayesâ?? theorem then provides the basic mechanism whereby measurements update these probabilities and, hence, our knowledge of the system state. For computer implementation of a Bayesian scheme, a representation of the probabilities must be selected. Various approaches have been developed, including Kalman filters, grid-based models, and particle filters.
This theory finds application in may technological fields, specially when a process must be predicted given a measurement set. The theorem provides a friendly way for the process prediction given a set of measures exploiting the theory such as Kalman's Filter and Hidden Markov Process.
For this reason in the years many efforts have been made to optimize the implementation techniques of the Bayes' Theorem by methods such as the Grid-Based and Particle Swarm.
The topic of this talk is to clarify the theory behind the Bayes theorem, and to show some use cases in the field of the network systems, of the theorem.