Phelma Formation 2022

Bayesian methods for data image analysis - WPMTBMD7

  • Number of hours

    • Lectures 13.5
    • Projects 0
    • Tutorials 1.5
    • Internship 0
    • Laboratory works 4.0

    ECTS

    ECTS 2.0

Goal(s)

The aim is to introduce fundamentals on Bayesian inference, and to develop applications in the framework of image and signal processing.

Contact Ronald PHLYPO

Content(s)

  • 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

Test

Semester 9 - The exam is given in english only 

Lab work + Written exam (First session)
+ Written or Oral exam for the second session



Contrôle continu *30% + DS 70%

Additional Information

Semester 9 - This course is given in english only EN

Course list
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.