Signal stochastic decomposition over continuous dictionaries
Résumé
We propose a Bayesian nonparametrics method, including algorithm for posterior computation, for the sparse regression problem. Our method applies in a general setting, when there are direct or indirect noisy observations of the signal. We try to make a wide focus on smoothness properties and sparsity of the approximate. As an example, we consider the ill-posed inverse problem of Quantum Homodyne Tomography.
Mots clés
Algorithms
Artificial intelligence
Computation theory
Inverse problems
Learning systems
Light sources
Stochastic models
Stochastic systems
Sparse regression
Stochastic decomposition
Noisy observations
ILL-posed inverse problem
Signal processing
Quantum homodyne tomography
Coorbit Theory
Bayesian nonparametrics