Bayesian Inference for Structured Spike and Slab Priors
Authors: Michael R Andersen, Ole Winther, Lars K. Hansen
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using numerical experiments on synthetic data, we demonstrate the benefits of the model. [...] This section describes a series of numerical experiments that have been designed and conducted in order to investigate the properties of the proposed algorithm. |
| Researcher Affiliation | Academia | Michael Riis Andersen, Ole Winther & Lars Kai Hansen DTU Compute, Technical University of Denmark DK-2800 Kgs. Lyngby, Denmark {miri, olwi, lkh}@dtu.dk |
| Pseudocode | Yes | The proposed algorithm is summarized in figure 2. [Figure 2: Proposed algorithm for approximating the joint posterior distribution over x, z and γ.] |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology. |
| Open Datasets | No | The paper uses synthetic data and refers to existing concepts like 'EEG source localization with synthetic sources [22]' and 'Shepp-Logan Phantom experiment from [2]', but it does not provide any specific links, DOIs, repositories, or formal citations for publicly available datasets used in its experiments. |
| Dataset Splits | No | The paper describes how problem instances were generated and evaluated, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We generate 100 problem instances from y = Ax0 + e, where the solutions vectors have been sampled from the proposed prior using the kernel Σi,j = 50 exp( ||i j||2 2/(2 102)), but constrained to have a fixed sparsity level of the K/D = 0.25. [...] The elements of A RN 250 are i.i.d Gaussian and the columns of A have been scaled to unit ℓ2-norm. The SNR is fixed at 20d B. [...] For the structured spike and slab method, we consider three different covariance structures: Σij = κ δ(i j), Σij = κ exp( ||i j||2/s) and Σij = κ exp( ||i j||2 2/(2s2)) with parameters κ = 50 and s = 10. In each case, we use a R = 50 rank approximation of Σ. [...] AEEG R128 800 is now a submatrix of a real EEG forward matrix corresponding to the grey area on the figure. The condition number of AEEG is 8 · 1015. The true sources X0 R800 20 are sampled from the structured spike and slab prior in eq. (8) using a squared exponential kernel with parameters A = 50, s = 10 and T = 20. The number of active sources is 46, i.e. x has 46 non-zero rows. SNR is fixed to 20d B. |