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Bayesian Inference in Digital Communication.Part I: Theory

Presented by Maurizio MAGARINI on 15 Oct 2012 from 14:00 to 16:00
Track: bayesian inference

Description

Given a hidden state process with memory and a measurement process that is memoryless given the state, one has to make inference on the hidden state sequence given the measurement sequence. This activity can be performed by the so-called Bayesian tracking, where the probability of the state at time k is recursively computed from the probability of the state at time k-1 and from the measurement at time k. The talk will recall the basics of Bayesian tracking: two-step recursion based on predict-update, forward-backward recursion for making inference on the state at time k given the entire measurement sequence. The Kalman filter is then presented as an instance of Bayesian tracking for a non-stationary linear state transition model and Gaussian processes. Wiener filter is then derived for the stationary case. The general case of non-linear and non-Gaussian model can be difficult to deal with. To get good results, the approach to be adopted should fit the specific state transition and measurement model at hand. As examples, the talk will present the non-Gaussian parametric approach, the state-space discretization approach, and the sequential importance sampling approach based on particle filters.

Place

Location: EGO, Cascina
Address: The European Gravitational Observatory (http://www.ego-gw.it), site of the Virgo interferometer, is located in the countryside of the Comune of Cascina, a few kilometres from town of Pisa.
Room: Auditorium