Mini Course 2 - Eric Moulines (CNRS/ENST)
Inference for Stochastic Processes
- Hidden Markov Models: from linear Gaussian state-spaces to non-linear models
- The Kalman filter
- An introduction to the particle filter
- The Baum-Welch algorithm
- Particle filter: some convergence results
- The auxiliary filter
- Parameter estimation I: theory and methods
- Parameter estimation II: algorithms
- The Filtering problem: stability, ergodicity
to download the notes.
to download the exercises.
To be also discussed: Limit theorems for weighted samples with applications to sequential monte carlo methods