Variational Bayesian Monte Carlo
Authors: Luigi Acerbi
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate VBMC both on several synthetic likelihoods and on a neuronal model with data from real neurons. Across all tested problems and dimensions (up to D = 10), VBMC performs consistently well in reconstructing the posterior and the model evidence with a limited budget of likelihood evaluations, unlike other methods that work only in very low dimensions. |
| Researcher Affiliation | Academia | Luigi Acerbi Department of Basic Neuroscience University of Geneva luigi.acerbi@unige.ch |
| Pseudocode | Yes | Algorithm 1 Variational Bayesian Monte Carlo Input: target log joint f, starting point x0, plausible bounds PLB, PUB, additional options |
| Open Source Code | Yes | Code available at https://github.com/lacerbi/vbmc. |
| Open Datasets | Yes | We consider a computational model of neuronal orientation selectivity in visual cortex [14]. We fit the neural recordings of one V1 and one V2 cell with the authors neuronal model that combines effects of filtering, suppression, and response nonlinearity [14]. |
| Dataset Splits | No | The paper describes using synthetic and real neuronal data, but does not specify explicit training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library or solver names with versions) are explicitly mentioned in the paper. |
| Experiment Setup | Yes | We set βLCB = 3 unless specified otherwise... At the beginning of each iteration after the first, VBMC actively samples nactive points (nactive = 5 by default in this work)... In each iteration, we collect ngp = round(80/n) samples... We set a maximum number of components Kmax = n2/3... For VBMC we set wmin = 0.01 and ε = 0.01... We define the current variational solution as improving if the ELCBO of the last iteration is higher than the ELCBO in the past few iterations (nrecent = 4)... The algorithm terminates when obtaining a stable solution for nstable = 8 iterations (with at most one non-stable iteration in-between). |