NextStatDeep dives into differentiable inference, GPU kernel design, and the engineering behind NextStat.
A NUTS implementation with artifact-driven benchmarks. ESS/leapfrog diagnostic isolates algorithmic efficiency from wall-time. Progressive sampling at the top-level tree join doubles ESS/leapfrog on GLM. 3.2× ESS/sec on hierarchical posteriors vs CmdStan 2.38.
Mathematical comparison of MoreFit (symbolic differentiation + JIT OpenCL) and NextStat (analytical CUDA kernels + ONNX flows + reverse-mode AD). Three-tier gradient system, fused NLL kernel, systematics, conditional flows, and R&D.
Extended unbinned likelihood mathematics, catalog of 10 parametric PDFs (Crystal Ball, KDE, Chebyshev, etc.), columnar EventStore (SoA), and a full resonance search workflow — from JSON spec to toy studies.
Why compile latency matters in scientific ML pipelines and how NextStat benchmarks cold-start vs warm throughput with reproducible harnesses.
Rigorous benchmarking of PK/NLME workflows: objective definitions, stopping rules, scaling protocols, correctness gates, and publishable artifacts.
How NextStat benchmarks Bayesian inference rigorously: ESS/sec methodology, stable protocols, diagnostics settings, and publishable artifacts for comparisons vs Stan and PyMC.
How NextStat benchmarks HistFactory inference engines without benchmark theater: correctness gates, optimizer convergence checks, warm-start policies, and auditable artifacts.
The strongest trust signal is an independent rerun. Same harness, published manifests, GPG/Sigstore signed reports, and verifiable validation_report.json with SHA-256 hashes.
How NextStat publishes benchmark snapshots as rerunnable artifact sets: CI automation, baseline manifests, correctness gates, and validation-report-backed evidence.
Scripts gave us speed. Reproducible benchmark harnesses give us trust. How performance claims become scientific experiments — with protocols, correctness gates, and auditable artifacts.
Public benchmark snapshots designed like experiments — with protocols, pinned environments, correctness gates, and artifacts that others can rerun. 6 suites across HEP, pharma, Bayesian, ML, time series, and econometrics.
Four-layer architecture: SoftHistogram → fused CUDA kernel → Rust GPU sessions → envelope theorem. Zero-copy device pointers, analytical gradients, 2.07e⁻⁹ accuracy.
3-way profile scan comparison: ROOT/RooFit vs pyhf vs NextStat. ROOT overestimates q(μ) on OverallSys, diverges on coupled HistoSys, fails catastrophically on EWK. 37×–880× faster.