Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Variational Bayesian Monte Carlo
Authors: Luigi Acerbi
NeurIPS 2018 | Venue PDF | 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 EMAIL |
| 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). |