Joint segmentation of wind speed and direction using a hierarchical model
Computational Statistics & Data Analysis
Hierarchical Bayesian sparse image reconstruction with application to MRFM
IEEE Transactions on Image Processing
Signal segmentation and modelling based on equipartition principle
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Lung nodule detection via Bayesian voxel labeling
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
IEEE Transactions on Signal Processing
Exploring the latent segmentation space for the assessment of multiple change-point models
Computational Statistics
| Hi-index | 35.69 |
Astronomy and other sciences often face the problem of detecting and characterizing structure in two or more related time series. This paper approaches such problems using Bayesian priors to represent relationships between signals with various degrees of certainty, and not just rigid constraints. The segmentation is conducted by using a hierarchical Bayesian approach to a piecewise constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results obtained with synthetic and real photon counting data illustrate the performance of the proposed algorithm