inv_gamma_map.h File Reference

Gamma-MAP sparse Bayesian inverse solver — automatic-relevance-determination prior on per-source variance hyperparameters. More...

#include "../inv_global.h"
#include "../inv_source_estimate.h"
#include <Eigen/Core>
#include <Eigen/Dense>
#include <QVector>

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Classes
struct	INVLIB::InvGammaMapResult
class	INVLIB::InvGammaMap
	Gamma-MAP sparse inverse solver. More...

Namespaces
namespace	INVLIB
	Inverse source estimation (MNE, dSPM, sLORETA, dipole fitting).

Detailed Description

Gamma-MAP sparse Bayesian inverse solver — automatic-relevance-determination prior on per-source variance hyperparameters.

SPDX-License-Identifier: BSD-3-Clause Copyright (c) 2026 MNE-CPP Authors

Author

Christoph Dinh chris.nosp@m.toph.nosp@m..dinh.nosp@m.@mne.nosp@m.-cpp..nosp@m.org

Since

2.2.0

Date

April 2026

INVLIB::InvGammaMap implements the Gamma-MAP / sparse Bayesian learning (SBL) solver of Wipf & Rao, NeuroImage 44(3), 947-966 (2009). The algorithm models source amplitudes as Gaussians with per-source variance hyperparameters \(\gamma_{i}\) and uses an EM / fixed-point update to iteratively re-estimate the \(\gamma\) vector from the data; sources whose \(\gamma_{i}\) collapses below threshold are pruned, leaving a sparse active set. Output is an InvGammaMapResult carrying the InvSourceEstimate, the surviving active-vertex list, the final \(\gamma\) vector, iteration count and residual norm.

Definition in file inv_gamma_map.h.