Results for “variational-inference”

Extract false-positive and false-negative gaps from VLM binary-classification-question predictions by comparing model responses against ground truth, producing a structured JSONL file and summary report for downstream root-cause analysis.

Builds a 3D volatility surface for any optionable ticker, mapping implied volatility across strike price and time to expiration to identify cheap, expensive, or anomalous options.

Annotates VCF variants using Ensembl VEP, ClinVar, and gnomAD, ranks them by predicted impact, and generates a reproducible report.

Create clear, effective data visualizations with chart selection, color theory, and annotation best practices using the inference.sh CLI.

Build, fit, validate, and compare Bayesian models using PyMC's modern API, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.