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uncertainty-quantification

uncertainty quantification and sensitivity analysis

Dakota, UQLab

SystemTechniques · 6Primitives · 31 uncertainty-quantificat… Batch checkpoint restartdistributed · medium: Large batch jobs (end-of-day, settlement, payroll runs) that checkpoint and restart idempotentlyDAG workflow executiondistributed · medium: Run a graph of dependent tasks by releasing each one when its inputs are done, longest chain first, and skip any task whose input hashes are unchanged.›Durable job queuedistributed · medium: Persisted tasks that workers lease for a visibility timeout, ack on success and retry with backoff; at-least-once execution that survives worker and queue crashes.›Bayesian optimizationml-training · medium: Surrogate model plus acquisition function (EI, UCB) to choose the next expensive evaluationMarkov chain monte carlonumerics · medium: Metropolis-Hastings and Gibbs sampling: proposals, burn-in, thinning, convergenceSequential monte carlonumerics · medium: Particle filters and SMC samplers with resampling for sequential Bayesian inferenceGaussian processesmachine-learning · low: GP regression with kernels: posterior mean/variance via Cholesky, hyperparameters by marginal likelihoodMcmc convergence diagnosticsnumerics · low: R-hat, effective sample size and divergence checks for MCMC runsPRNGs (PCG, xoshiro)numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.›Importance samplingnumerics · low: Sample from a proposal distribution and reweight to cut Monte Carlo varianceRegularizationmachine-learning · low: Techniques that trade training fit for generalization: weight decay, dropout, early stopping, label smoothing and data mixing, with the exact update rules and defaults.›Gaussian quadraturenumerics · low: Approximate an integral by a weighted sum of n function values at roots of orthogonal polynomials; exact for polynomials up to degree 2n-1.›Low discrepancy sequencesnumerics · low: Sobol, Halton and blue-noise sequences with scrambling for quasi-Monte CarloMonte Carlo integrationnumerics · low: Estimate an expectation by averaging random samples; error falls as σ/√N in any dimension, and variance reduction (antithetic, control variates, importance sampling) shrinks σ.›Bootstrap confidence intervalsstatistics · low: Resampling-based confidence intervals for arbitrary statisticsIdempotency keysfinance · low: Client-chosen unique key per logical operation, stored with a request fingerprint and the final response, so retries replay the first result instead of acting twice.›Write-ahead logstorage · low: Append each change to a checksummed sequential log and fsync it before touching data pages; the basis of crash durability in nearly every database.›Cron schedulingsystems · low: Cron expression parsing and next-fire-time computation, with catch-up and time-zone rulesTopological sortgraphs · low: Order the vertices of a DAG so every edge points forward; the core of build systems, schedulers and dependency resolution, with cycle detection for free.›Content-addressed storagestorage · low: Store each blob under the hash of its bytes: immutable, deduplicated by construction, self-verifying on every read, and composable into Merkle trees.›Exponential backoff with jittersystems · low: Retry after delays that grow geometrically and are randomized, so failed clients spread out instead of hammering a recovering service in synchronized waves.›Dead-letter queuedistributed · low: A side queue for messages that keep failing: after N delivery attempts or a permanent error, park them with the reason so the main queue keeps moving.›+ 15 more primitives

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