Number of hours
- Lectures 13.5
- Projects 0
- Tutorials 1.5
- Internship 0
- Laboratory works 4.0
The aim is to introduce fundamentals on Bayesian inference, and to develop applications in the framework of image and signal processing.
- Bayesian estimators
- Priori choice
- Approximate Bayesian inference
- Deterministic approximation methods
- Stochastic approximation methods
- Case study: Bayesian inference for speech recognition
Basic notion in both estimation and detection theory
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%
Semester 9 - This course is given in english only
 Robert, C. (2007). The Bayesian choice: from decision-theoretic foundations to computational implementation. Springer Science & Business Media.
 Šmídl, V., & Quinn, A. (2006). The variational Bayes method in signal processing. Springer Science & Business Media.
 Gilks, W. R. (2005). Markov chain monte carlo. John Wiley & Sons, Ltd.
 Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.). (2010). Bayesian nonparametrics (Vol. 28). Cambridge University Press.