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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


Prerequisites

Basic 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 EN

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

Université Grenoble Alpes