Sequential Bayesian & bandits
Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.
50 of 388 tools.
Allocate restless bandit interventions
Allocate scarce recurring interventions across evolving Markov units with Whittle indices, explicit indexability checks, and paired policy simulation.
Audit code knowledge concentration integrity
Audit file, module, service, or repository knowledge concentration from point-in-time substantive changes, reviews, incident response and documentation using identity-confidence filtering, recency decay, Bayesian ownership uncertainty, entropy-effective owners, HHI and leave-top-owner-out resilience—without turning contribution evidence into a person-performance score.
Audit joint outcome network integrity
Audit whether a company-specific Bayesian joint-outcome network is fit for reliance by validating point-in-time lineage, DAG and CPT completeness, effective support, protected-attribute exclusions, and strictly out-of-time outcome calibration against a baseline.
Audit selective label partial identification
Partially identify event risk, calibration gap, and Brier score when a policy selectively reveals outcomes: retain missing labels, model observed-versus-missing event odds within each decision stratum under a governed sensitivity ratio, propagate Beta posterior uncertainty, expose unsupported strata and label coverage, and fail closed on wide bounds or undocumented decision rules.
Bayesian account risk triage
Prioritize human review of auditable account-security and policy-conflict evidence using Bayes factors and decision costs.
Calculate earned value forecast
Turn period-level planned value, accepted earned value, and actual cost into a correlated Bayesian CPI/SPI distribution for final cost, completion period, budget overrun, and deadline miss, with classical EAC cross-checks and an early-progress abstention gate.
Cluster process markov archetypes
Discover privacy-eligible workflow archetypes from aggregate Markov transition counts using empirical-Bayes shrinkage, Jensen-Shannon k-medoids, silhouette quality, and posterior assignment stability.
Estimate cannibalization adjusted feature value
Estimate feature value after posterior cannibalization of legacy contribution, using aligned adoption scenarios, beta-binomial substitution uncertainty, and value plus substitution-risk gates.
Estimate decision reversal probability
Estimate how often planned evidence would reverse the current decision under a correlated Bayesian preposterior model, while separating fragility, regret, and net information value.
Estimate pricing experiment value
Choose pricing experiment arms by posterior future contribution, conversion-harm probability, and a model-conditional perfect-information value upper bound.
Estimate transportable root cause probability
Estimate how likely a mechanism actually caused an observed failure using transport-weighted Bayesian random-effects MCMC across remediation studies, posterior probability of necessity, convergence diagnostics and mandatory unmeasured-confounding sensitivity.
Forecast AI capability atrophy loss
Learn how aggregate fallback capability decays with AI reliance and is preserved by unaided practice using a Bayesian right-censored transition model, then forecast ready/degraded/unavailable capacity and correlated provider-outage economic VaR/CVaR.
Forecast AI code maintenance liability
Forecast the long-run maintenance liability of aggregate AI-assisted code inventory with a Bayesian Gamma-Poisson recurrent-event model, learned AI/complexity/age hazards, lognormal severity and correlated repository shock VaR/CVaR.
Forecast AI evaluation production validity
Forecast whether offline AI evaluation scores will remain valid in production using a tenant-pooled Bayesian logit calibration with workload effects and time drift, coherent operating scenarios, false-promotion risk, breach timing, net value and quality-shortfall VaR/CVaR.
Forecast AI output IP claim liability
Forecast aggregate AI-output IP claim frequency, gross cost, defense and disruption, collectible indemnity and net VaR/CVaR using tenant-local Bayesian recurrence/severity evidence plus shared provider events and counterparty default.
Forecast AI privacy attack loss
Forecast correlated membership-inference or reconstruction loss from tenant-local member/nonmember red-team trials using Beta posterior attack advantage, partially pooled lognormal harm, binomial subject exposure, coherent attacker regimes, common asset-group compromise, control effects and loss VaR/CVaR.
Forecast AI route quality cost drift
Forecast route-level quality, inference cost, p95 latency, breach timing, net value and economic-loss VaR/CVaR with partially pooled Bayesian trends and one common disruption state shared across every route on the same provider.
Forecast analytics calibration survival
Forecast how long each locally calibrated analytical function remains decision-safe using right-censored calibration episodes, a discrete empirical-Bayes failure hazard, conditional survival from current calibration age, posterior uncertainty and explicit endpoint-support gates.
Forecast change adoption bass diffusion
Forecast aggregate organizational change or tool adoption with a Bayesian Bass diffusion model learned from reconciled historical cohorts, jointly estimating spontaneous innovation and imitation, simulating posterior uptake under per-cohort enablement capacity, pricing enabled value, exposing grid-boundary misspecification, and gating a target adoption probability.
Forecast engineering investment benefit realization
Forecast whether an engineering-investment portfolio will realize finance-defined benefits within a decision horizon using a partially pooled Bayesian hurdle/lognormal model for zero-benefit risk, positive benefit multiples, and realization lag; correlated organization shocks; discounting; NPV/ROI gates; and explicit unseen-category fallback.
Forecast executive technology commitment credibility
Recalibrate executive technology commitments with class-local isotonic Beta posteriors, then simulate correlated on-time outcomes, conditional lognormal delay, value erosion and financial-shortfall VaR/CVaR.
Forecast feature adoption revenue
Forecast feature adoption, revenue, and contribution with a grouped discrete-time hazard model trained on reconciled censored cohorts, required to beat a pooled-hazard baseline on later cohorts before posterior and capacity-constrained forecasts are decision-safe.
Forecast governed attrition competing risks
Forecast voluntary departure, internal transfer and involuntary exit as calibrated discrete-time competing risks with company-local chronological validation, peer partial pooling, posterior intervals and an automatic abstention when the model does not beat role base rates.
Forecast governed release competing risks
Forecast company-local rollback, hotfix and incident incidence conditional on deployment with inverse-propensity-corrected discrete-time competing risks, strict whole-release chronological validation, posterior intervals and mandatory improvement over both a simple baseline and the legacy PR score.
Forecast growth channel response saturation
Learn organization-specific channel saturation from resolved aggregate incrementality estimates: fit a likelihood-weighted Bayesian grid of Hill response curves, reserve the newest periods for honest validation against a linear baseline, expose posterior boundary misspecification and evidence failures, and return contribution and marginal-return distributions for proposed spend levels.
Forecast intervention effect half life
Learn how quickly a governed intervention's effect decays across resolved cohorts using a shared exponential half-life, cohort-specific amplitudes, a persistent floor, reported standard errors, and a profiled Bayesian grid; then forecast effect/value paths and when each current intervention is likely to fall below a practical threshold.
Forecast joint engineering outcome distribution
Learn a company-local partially pooled discrete Bayesian network from complete mature observations, validate it strictly out of time against an independent baseline, and answer coherent conditional joint engineering-outcome queries with exact inference and Dirichlet posterior intervals.
Forecast knowledge continuity semimarkov
Forecast critical code-knowledge continuity with a company-local hierarchical Bayesian semi-Markov model whose state-exit hazard depends on time already resilient, concentrated, orphaned or recovering; require a strict latest-period holdout improvement over persistence, simulate coherent common shocks, and expose orphaning, delay, recovery-cost and portfolio VaR/CVaR without predicting named departures.
Forecast shadow AI unseen exposure loss
Estimate AI services missed by every detector with a Bayesian zero-truncated binomial capture–recapture model, then simulate scenario-dependent visibility, incidents, common provider exposure, usage, value disruption and lognormal loss to produce unseen-inventory and economic VaR/CVaR tails.
Forecast workflow absorption semimarkov
Forecast terminal workflow outcomes and remaining time from Bayesian transition and lognormal dwell-time posteriors over status histories.
Infer competing root cause posterior
Rank competing, compound and unknown root mechanisms from company-local resolved incidents using partially pooled Dirichlet-Beta learning, strict temporal holdout scoring, reliability-tempered signals and posterior uncertainty rather than a single brittle traceback winner.
MCMC project completion forecast
Forecast live task and project completion with a censored Bayesian lognormal model, MCMC uncertainty, dependencies, and finite parallelism.
Monitor forecast calibration eprocess
Continuously monitor binary forecasts for calibration drift with an anytime-valid mixture e-process that does not incur a repeated-peeking penalty.
Monitor sequential intervention experiment
Monitor cumulative binary intervention outcomes with beta-binomial posteriors, Bayes factors, expected regret, and preregistered success, harm, or futility stopping.
Optimize adaptive analytics plan
Choose an exact adaptive sequence of analyses and an outcome-contingent terminal action by Bayesian belief-state dynamic programming, allowing early stopping while enforcing cost, duration, dependency, exclusion and analysis-step constraints and measuring value over the best fixed analysis sequence.
Optimize AI evaluation value of information portfolio
Select additional AI evaluation plans by multi-stratum posterior-predictive value of sample information, discounting duplicate content and optimizing budget, reviewer capacity, delay, quality lower bounds and expected/CVaR incremental false-deployment loss with exact or disclosed beam search.
Optimize calibration experiment portfolio
Choose which analytical functions to calibrate next with exact Beta-binomial posterior-predictive value of sample information, result-contingent activation thresholds, false-activation loss, experiment budget/capacity, dependencies, exclusions and exact-or-disclosed portfolio search.
Optimize error budget portfolio
Choose dependency-safe reliability interventions under money and capacity constraints using posterior SLO-breach economics.
Optimize identity assurance response portfolio
Choose one preauthorized identity-assurance response per aggregate account-risk case by maximizing simulated net access value minus security, false-positive, operating and CVaR costs under budget, capacity, control, availability and due-process constraints.
Optimize learning vs earning allocation
Solve the exact finite-horizon Beta–Bernoulli bandit for allocating scarce units between a known earning baseline and uncertain actions that earn and update their posterior.
Optimize preventive maintenance policy
Optimize preventive replacement or refactoring intervals with Bayesian-scenario Weibull renewal-reward economics and a worst-case cost penalty.
Optimize safe AI routing exploration portfolio
Allocate bounded production traffic to one safe challenger per AI workload by posterior-predictive knowledge gradient, maximizing net learning value under local quality/harm evidence, privacy, latency, provider diversity, shared endpoint capacity, exploration budget, provider concentration and regret CVaR constraints.
Optimize sample size by decision value
Choose a two-arm experiment sample size by Bayesian expected value of sample information after implementation economics, sampling cost, posterior adoption and harm gates, regret, and Monte Carlo recommendation precision.
Optimize sovereign data placement portfolio
Choose one executable regional placement per governed data workload by Monte Carlo posterior risk and exact/beam Pareto search under hard residency, KMS, encryption, diversity, latency, availability, relation, budget and capacity constraints.
Rank experiments by expected information gain
Rank prospective experiments by Bayesian mutual information and decision-aware expected value of sample information across explicit hypotheses, result likelihoods and decision payoffs; price usability, monetary cost and decision delay, expose recommendation-change probability, and preserve a value-information-cost-delay Pareto set.
Recommend safe contextual bandit action
Recommend contextual aggregate interventions with Bayesian reward learning only inside a posterior logistic harm constraint, explicitly falling back to a governed baseline when no arm is safe enough.
Simulate contextual thompson bandit
Simulate Bayesian contextual Thompson sampling and quantify intervention reward, regret, and policy uncertainty.
Solve bayesian influence diagram
Solve an exact discrete Bayesian influence diagram over actions, chance-node DAGs, action-dependent conditional probabilities, pre-decision evidence, and additive utility tables, then quantify action regret and the expected value of perfect information for observable exogenous nodes.
Solve budgeted bayesian experiment portfolio
Choose a budget- and resource-feasible portfolio of Bayesian experiments whose correlated observations can change multiple governed deployment decisions.
Value next round option
Value raising now versus delaying for a milestone by simulating posterior milestone success, bridge-capacity failure, conditional future dilution, terminal stakeholder value, and lower-tail delay loss.