Bayesian methods for data image analysis (SIGMA S9) - WPMTBMD7
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Number of hours
- Lectures : 15.0
- Tutorials : 0
- Laboratory works : 4.0
- Projects : 0
- Internship : 0
ECTS : 2.0
Goals
The aim is to introduce fundamentals on Bayesian inference, and to develop applications in the framework of image and signal processing.
Content - Introduction
- Bayesian estimators
- Priori choice
- Approximate Bayesian inference
- Deterministic approximation methods
- Stochastic approximation methods
- Case study: Bayesian inference for speech recognition
PrerequisitesBasic notion in both estimation and detection theory
Tests Semester 9 - The exam is given in english only 
Written exam
Contrôle continu *30% + DS 70%
Additional Information Semester 9 - This course is given in english only 
Curriculum->Double-Diploma Engineer/Master->Semester 9
Curriculum->Master->Semester 9
Bibliography [1] Robert, C. (2007). The Bayesian choice: from decision-theoretic foundations to computational implementation. Springer Science & Business Media.
[2] Šmídl, V., & Quinn, A. (2006). The variational Bayes method in signal processing. Springer Science & Business Media.
[3] Gilks, W. R. (2005). Markov chain monte carlo. John Wiley & Sons, Ltd.
[4] Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.). (2010). Bayesian nonparametrics (Vol. 28). Cambridge University Press.
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Date of update January 9, 2017