Bayesian approach with prior models which enforce sparsity in signal and image processing
A Mohammad-Djafari - EURASIP Journal on Advances in Signal …, 2012 - Springer
In this review article, we propose to use the Bayesian inference approach for inverse
problems in signal and image processing, where we want to infer on sparse signals or …
problems in signal and image processing, where we want to infer on sparse signals or …
Bayesian inference for inverse problems
A Mohammad-Djafari - AIP Conference Proceedings, 2002 - pubs.aip.org
Traditionally, the MaxEnt workshops start by a tutorial day. This paper summarizes my talk
during 2001'th workshop at John Hopkins University. The main idea in this talk is to show …
during 2001'th workshop at John Hopkins University. The main idea in this talk is to show …
Bayesian analysis of ODEs: solver optimal accuracy and Bayes factors
In most cases in the Bayesian analysis of ODE inverse problems, a numerical solver needs
to be used. Therefore, we cannot work with the exact theoretical posterior distribution but …
to be used. Therefore, we cannot work with the exact theoretical posterior distribution but …
Numerical posterior distribution error control and expected Bayes Factors in the bayesian Uncertainty Quantification of Inverse Problems
In the bayesian analysis of Inverse Problems most relevant cases the forward maps (FM, or
regressor function) are defined in terms of a system of (O, P) DE's with intractable solutions …
regressor function) are defined in terms of a system of (O, P) DE's with intractable solutions …
Nonparametric posterior learning for emission tomography with multimodal data
F Goncharov, É Barat, T Dautremer - arXiv preprint arXiv:2108.00866, 2021 - arxiv.org
We continue studies of the uncertainty quantification problem in emission tomographies
such as PET or SPECT when additional multimodal data (eg, anatomical MRI images) are …
such as PET or SPECT when additional multimodal data (eg, anatomical MRI images) are …
Bayesian inference with hierarchical prior models for inverse problems in imaging systems
A Mohammad-Djafari - … on Systems, Signal Processing and their …, 2013 - ieeexplore.ieee.org
Bayesian approach is nowadays commonly used for inverse problems. Simple prior laws
(Gaussian, Generalized Gaussian, Gauss-Markov and more general Markovian priors) are …
(Gaussian, Generalized Gaussian, Gauss-Markov and more general Markovian priors) are …
Dynamic and clinical PET data reconstruction: A nonparametric Bayesian approach
MD Fall, É Barai, C Comtat, T Dautremer… - … on Image Processing, 2013 - ieeexplore.ieee.org
We propose a nonparametric and Bayesian method for reconstructing dynamic Positron
Emission Tomography (PET) images from clinical data. PET is a nuclear medicine imaging …
Emission Tomography (PET) images from clinical data. PET is a nuclear medicine imaging …
Continuous space-time reconstruction in 4D PET
MD Fall, É Barat, C Comtat, T Dautremer… - 2011 IEEE Nuclear …, 2011 - ieeexplore.ieee.org
The aim of this work is to propose a method for reconstructing space-time 4D PET images
directly from the data without any discretization, neither in space nor in time. To accomplish …
directly from the data without any discretization, neither in space nor in time. To accomplish …
Bayesian Nonparametrics and Biostatistics: The Case of PET Imaging
MD Fall - The International Journal of Biostatistics, 2019 - degruyter.com
Biostatistic applications often require to collect and analyze a massive amount of data.
Hence, it has become necessary to consider new statistical paradigms that perform well in …
Hence, it has become necessary to consider new statistical paradigms that perform well in …
Voxel-Based Lesion-Symptom Mapping: A Nonparametric Bayesian Approach
MD Fall, E Lavau, P Auzou - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
The study of brain-injured patients (or lesion-based analysis) is a powerful paradigm for
investigating structure-function relationships using neuroimaging techniques. Voxel-based …
investigating structure-function relationships using neuroimaging techniques. Voxel-based …