Dynamic Bayesian Network for Time-Dependent Classification Problems in Robotics
( Bayesian Inference )
Publication series ： Bayesian Inference
Author： Cristiano Premebida Francisco A. A. Souza and Diego R. Faria
Publisher： IntechOpen
Publication year： 2017
E-ISBN: INT6626470059
P-ISBN(Paperback): 9789535135777
P-ISBN(Hardback): 9789535135784
Subject： O211 probability (probability theory, probability theory)
Keyword： 概率论（几率论、或然率论）
Language： ENG
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Dynamic Bayesian Network for Time-Dependent Classification Problems in Robotics
Description
This chapter discusses the use of dynamic Bayesian networks (DBNs) for time-dependent classification problems in mobile robotics, where Bayesian inference is used to infer the class, or category of interest, given the observed data and prior knowledge. Formulating the DBN as a time-dependent classification problem, and by making some assumptions, a general expression for a DBN is given in terms of classifier priors and likelihoods through the time steps. Since multi-class problems are addressed, and because of the number of time slices in the model, additive smoothing is used to prevent the values of priors from being close to zero. To demonstrate the effectiveness of DBN in time-dependent classification problems, some experimental results are reported regarding semantic place recognition and daily-activity classification.
Chapter