tap gibbs free energy
TAP Gibbs Free Energy, Belief Propagation and Sparsity
The adaptive TAP Gibbs free energy for a general densely connected probabilistic model with quadratic interactions and arbritary single site constraints is derived. We show how a specific sequential minimization of the free energy leads to a generalization of Minka's expectation propa- gation. Lastly, we derive a sparse representation version of the sequential algorithm. The usefulness of the approach is demonstrated on classifica- tion and density estimation with Gaussian processes and on an indepen- dent component analysis problem.
TAP Gibbs Free Energy, Belief Propagation and Sparsity
Csató, Lehel, Opper, Manfred, Winther, Ole
The adaptive TAP Gibbs free energy for a general densely connected probabilistic model with quadratic interactions and arbritary single site constraints is derived. We show how a specific sequential minimization of the free energy leads to a generalization of Minka's expectation propagation. Lastly, we derive a sparse representation version of the sequential algorithm. The usefulness of the approach is demonstrated on classification and density estimation with Gaussian processes and on an independent component analysis problem.
TAP Gibbs Free Energy, Belief Propagation and Sparsity
Csató, Lehel, Opper, Manfred, Winther, Ole
The adaptive TAP Gibbs free energy for a general densely connected probabilistic model with quadratic interactions and arbritary single site constraints is derived. We show how a specific sequential minimization of the free energy leads to a generalization of Minka's expectation propagation. Lastly, we derive a sparse representation version of the sequential algorithm. The usefulness of the approach is demonstrated on classification and density estimation with Gaussian processes and on an independent component analysis problem.
TAP Gibbs Free Energy, Belief Propagation and Sparsity
Csató, Lehel, Opper, Manfred, Winther, Ole
The adaptive TAP Gibbs free energy for a general densely connected probabilistic model with quadratic interactions and arbritary single site constraints is derived. We show how a specific sequential minimization of the free energy leads to a generalization of Minka's expectation propagation. Lastly,we derive a sparse representation version of the sequential algorithm. The usefulness of the approach is demonstrated on classification anddensity estimation with Gaussian processes and on an independent componentanalysis problem.