Dependent relevance determination for smooth and structured sparse regression

Keyphrases

• Sparse Regression 100%
• Structured Sparse Representation 100%
• Prior Variance 100%
• Laplace Approximation 100%
• Parameter Vector 50%
• Sparsity 50%
• Nonzero Coefficients 50%
• Linear Regression 50%
• Weight Vector 50%
• Markov Chain Monte Carlo 50%
• Regression Method 50%
• Hierarchical Model 50%
• Approximation Approach 50%
• Fourier Coefficients 50%
• Smooth Region 50%
• Gaussian Process 50%
• Brain Imaging 50%
• Non-convexity 50%
• Different Bases 50%
• Setting Parameters 50%
• Least Absolute Shrinkage and Selection Operator (LASSO) 50%
• Regression Weights 50%
• Structured Model 50%
• Convex Relaxation 50%
• Sparse Weight 50%
• Automatic Relevance Determination 50%
• Markov Chain Sampling 50%

Mathematics

• Variance 100%
• Laplace Approximation 100%
• Parameter Vector 50%
• Tensor 50%
• Markov Chain Monte Carlo 50%
• Fourier Coefficient 50%
• Gaussian Process 50%
• Hierarchical Model 50%
• Linear Regression 50%