Abstract
In this chapter we discuss signal parameters estimation and its application to social multimedia signals. Given a random observation Y taking values in an observation set Г, we wish to estimate the values of some quantities that are not observed directly but are related to Y. The connection between the observation and the desired quantities is probabilistic in the sense that the statistical behavior of Y is influenced by the values of quantities to be estimated. This can be modeled by a family of probability distributions on Г, indexed by the quantities to be estimated. Our goal is then to find an optimum way of processing the observation Y in order to estimate as accurately as possible the values of the desired quantities.
Keywords
- Bayesian Network
- Minimum Mean Square Error
- Latent Dirichlet Allocation
- Recurrence Score
- Membership Score
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Roy, S.D., Zeng, W. (2015). Estimation: The Empirical Judgment. In: Social Multimedia Signals. Springer, Cham. https://doi.org/10.1007/978-3-319-09117-4_6
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DOI: https://doi.org/10.1007/978-3-319-09117-4_6
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